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Background: With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke and its risk factors and conducts high-risk population interventions for people aged above 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, smoking, lack of exercise, apparently overweight and family history of stroke. People with more than two risk factors or history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk levels for people with unknown values in fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in statistical results at the national level. In this paper, we use 2017 national stroke screening data to develop stroke risk classification models based on machine learning algorithms to improve the classification efficiency.Method: Firstly, we construct training set and test sets and process the imbalance training set based on oversampling and undersampling method. Then, we develop logistic regression model, Naïve Bayesian model, Bayesian network model, decision tree model, neural network model, random forest model, bagged decision tree model, voting model and boosting model with decision trees to classify stroke risk levels.Result: The recall of the boosting model with decision trees is the highest (99.94%), and the precision of the model based on the random forest is highest (97.33%). Using the random forest model (recall: 98.44%), the recall will be increased by about 2.8% compared with the method currently used, and several thousands more people with high risk of stroke can be identified each year.Conclusion: Models developed in this paper can improve the current screening method in the way that it can avoid the impact of unknown values, and avoid unnecessary rescreening and intervention expenditures. The national stroke screening program can choose classification models according to the practice need. [ABSTRACT FROM AUTHOR]

Background: Achieving healthy ageing has become the only way for China to alleviate the pressure of ageing, especially in rural areas. However, the factors affecting the health of rural older adults are numerous and complex. It is important to identify the critical factors that affecting the health of older adults in rural areas and provide decision-making support for targeted health interventions.Methods: To overcome some limitations of existing works, an extended probabilistic linguistic fuzzy cognitive map model is proposed in this paper as a useful tool for modeling the cause-effect relationship between factors. The proposed model integrates the advantages of probabilistic linguistic term sets and fuzzy cognitive maps. In the end, to rank and identify the critical factors affecting the health, a novel similarity measure based on Euclidean distance and Z-mapping function is proposed.Results: The proposed model can effectively deal with the uncertainty of experts and reflect different opinions of groups well. In terms of representing uncertainty and ambiguity, the proposed method outperforms other models in modeling complex systems. In the real-world case analysis, we find that education is the most important factor affecting the health of rural older adults, followed by previous occupational experiences, psychology, and physical exercise, among other things. Intergenerational relationship has become another important factor affecting the health of rural older adults in China as the development of Chinese society.Conclusions: From a macro perspective, social economic status, living environment, lifestyle, and health management, are the variables that have the greatest impact on the health of rural older adults. As a result, providing more precise health interventions with the characteristics of factors influencing health is a crucial guarantee for preserving and improving the health of rural older adults in China. [ABSTRACT FROM AUTHOR]

Background: Standardized coding of plays an important role in radiology reports' secondary use such as data analytics, data-driven decision support, and personalized medicine. RadLex, a standard radiological lexicon, can reduce subjective variability and improve clarity in radiology reports. RadLex coding of radiology reports is widely used in many countries, but translation and localization of RadLex in China are far from being established. Although automatic RadLex coding is a common way for non-standard radiology reports, the high-accuracy cross-language RadLex coding is hardly achieved due to the limitation of up-to-date auto-translation and text similarity algorithms and still requires further research.Methods: We present an effective approach that combines a hybrid translation and a Multilayer Perceptron weighting text similarity ensemble algorithm for automatic RadLex coding of Chinese structured radiology reports. Firstly, a hybrid way to integrate Google neural machine translation and dictionary translation helps to optimize the translation of Chinese radiology phrases to English. The dictionary is made up of 21,863 Chinese-English radiological term pairs extracted from several free medical dictionaries. Secondly, four typical text similarity algorithms are introduced, which are Levenshtein distance, Jaccard similarity coefficient, Word2vec Continuous bag-of-words model, and WordNet Wup similarity algorithms. Lastly, the Multilayer Perceptron model has been used to synthesize the contextual, lexical, character and syntactical information of four text similarity algorithms to promote precision, in which four similarity scores of two terms are taken as input and the output presents whether the two terms are synonyms.Results: The results show the effectiveness of the approach with an F1-score of 90.15%, a precision of 91.78% and a recall of 88.59%. The hybrid translation algorithm has no negative effect on the final coding, F1-score has increased by 21.44% and 8.12% compared with the GNMT algorithm and dictionary translation. Compared with the single similarity, the result of the MLP weighting similarity algorithm is satisfactory that has a 4.48% increase compared with the best single similarity algorithm, WordNet Wup.Conclusions: The paper proposed an innovative automatic cross-language RadLex coding approach to solve the standardization of Chinese structured radiology reports, that can be taken as a reference to automatic cross-language coding. [ABSTRACT FROM AUTHOR]

Background: Text Matching (TM) is a fundamental task of natural language processing widely used in many application systems such as information retrieval, automatic question answering, machine translation, dialogue system, reading comprehension, etc. In recent years, a large number of deep learning neural networks have been applied to TM, and have refreshed benchmarks of TM repeatedly. Among the deep learning neural networks, convolutional neural network (CNN) is one of the most popular networks, which suffers from difficulties in dealing with small samples and keeping relative structures of features. In this paper, we propose a novel deep learning architecture based on capsule network for TM, called CapsTM, where capsule network is a new type of neural network architecture proposed to address some of the short comings of CNN and shows great potential in many tasks.Methods: CapsTM is a five-layer neural network, including an input layer, a representation layer, an aggregation layer, a capsule layer and a prediction layer. In CapsTM, two pieces of text are first individually converted into sequences of embeddings and are further transformed by a highway network in the input layer. Then, Bidirectional Long Short-Term Memory (BiLSTM) is used to represent each piece of text and attention-based interaction matrix is used to represent interactive information of the two pieces of text in the representation layer. Subsequently, the two kinds of representations are fused together by BiLSTM in the aggregation layer, and are further represented with capsules (vectors) in the capsule layer. Finally, the prediction layer is a connected network used for classification. CapsTM is an extension of ESIM by adding a capsule layer before the prediction layer.Results: We construct a corpus of Chinese medical question matching, which contains 36,360 question pairs. This corpus is randomly split into three parts: a training set of 32,360 question pairs, a development set of 2000 question pairs and a test set of 2000 question pairs. On this corpus, we conduct a series of experiments to evaluate the proposed CapsTM and compare it with other state-of-the-art methods. CapsTM achieves the highest F-score of 0.8666.Conclusion: The experimental results demonstrate that CapsTM is effective for Chinese medical question matching and outperforms other state-of-the-art methods for comparison. [ABSTRACT FROM AUTHOR]

Background: Many models are published which predict outcomes in hospitalized COVID-19 patients. The generalizability of many is unknown. We evaluated the performance of selected models from the literature and our own models to predict outcomes in patients at our institution.Methods: We searched the literature for models predicting outcomes in inpatients with COVID-19. We produced models of mortality or criticality (mortality or ICU admission) in a development cohort. We tested external models which provided sufficient information and our models using a test cohort of our most recent patients. The performance of models was compared using the area under the receiver operator curve (AUC).Results: Our literature review yielded 41 papers. Of those, 8 were found to have sufficient documentation and concordance with features available in our cohort to implement in our test cohort. All models were from Chinese patients. One model predicted criticality and seven mortality. Tested against the test cohort, internal models had an AUC of 0.84 (0.74-0.94) for mortality and 0.83 (0.76-0.90) for criticality. The best external model had an AUC of 0.89 (0.82-0.96) using three variables, another an AUC of 0.84 (0.78-0.91) using ten variables. AUC's ranged from 0.68 to 0.89. On average, models tested were unable to produce predictions in 27% of patients due to missing lab data.Conclusion: Despite differences in pandemic timeline, race, and socio-cultural healthcare context some models derived in China performed well. For healthcare organizations considering implementation of an external model, concordance between the features used in the model and features available in their own patients may be important. Analysis of both local and external models should be done to help decide on what prediction method is used to provide clinical decision support to clinicians treating COVID-19 patients as well as what lab tests should be included in order sets. [ABSTRACT FROM AUTHOR]

Background: It is significant to model clinical activities for process mining, which assists in improving medical service quality. However, current process mining studies in healthcare pay more attention to the control flow of events, while the data properties and the time perspective are generally ignored. Moreover, classifying event attributes from the view of computers usually are difficult for medical experts. There are also problems of model sharing and reusing after it is generated.Methods: In this paper, we presented a constraint-based method using multi-perspective declarative process mining, supporting healthcare personnel to model clinical processes by themselves. Inspired by openEHR, we classified event attributes into seven types, and each relationship between these types is represented in a Constrained Relationship Matrix. Finally, a conformance checking algorithm is designed.Results: The method was verified in a retrospective observational case study, which consists of Electronic Medical Record (EMR) of 358 patients from a large general hospital in China. We take the ischemic stroke treatment process as an example to check compliance with clinical guidelines. Conformance checking results are analyzed and confirmed by medical experts.Conclusions: This representation approach was applicable with the characteristic of easily understandable and expandable for modeling clinical activities, supporting to share the models created across different medical facilities. [ABSTRACT FROM AUTHOR]

Background: The social Q&A community quickly becomes a popular platform for consumers to find health information because of its convenience and interactivity.Methods: Based on the 10,861 depression questions collected in the Zhihu, the largest Q&A platform in China, we divided the healthy information needs description into nine categories with Latent Dirichlet Allocation (LDA). We also divided the healthy information needs type into Physiological, affective and cognitive needs based on the Wilson model.Results: The results show that the largest categories are depression symptom and social activities while the less concerned health information is prevention and medical insurance. More attention is paid to cognitive needs. We also find there is no strong correlation between attention and needs type.Conclusions: The purpose of this paper is to refine the consumer health information needs types to better understand the consumer health information characteristic in China. [ABSTRACT FROM AUTHOR]

Background: Due to the increasing complexity in socioeconomic environments and the ambiguity in human cognition, decision makers prefer to give linguistic cognitive information with different granularities according to their own preferences. Consequently, to consider the uncertainty and preferences in the evaluation process, a method based on Multi-Granularity Linguistic Information (MGLI) for evaluating teleconsultation service quality is proposed, which provides a new research direction for scientific evaluation and improvement of teleconsultation service quality.Methods: Firstly, this paper explored a service quality evaluation system from the perspective of regional doctors. And then considering the uncertainty and preferences of decision makers, MGLI was used to optimize the index system according to the similarity degree between the linguistic evaluation information and a given linguistic term set. Finally, the empirical research was conducted using Henan Province Telemedicine Center of China (HTCC) as an example to identify the direction for improving the service quality in teleconsultation.Results: This study found that the number of consulting rooms, attitude of operators, consultation duration, charges, and attitude of experts are the key factors affecting the quality of teleconsultation service.Conclusions: Suggestions for improving the quality of teleconsultation service are put forward in terms of optimizing the allocation of consulting rooms, improving regional doctors' experience and standardizing charging standards, which provides a new direction for improving the quality of teleconsultation service. [ABSTRACT FROM AUTHOR]

Background: Traditional Chinese medicine (TCM) is a highly important complement to modern medicine and is widely practiced in China and in many other countries. The work of Chinese medicine is subject to the two factors of the inheritance and development of clinical experience of famous Chinese medicine practitioners and the difficulty in improving the service capacity of basic Chinese medicine practitioners. Heterogeneous information networks (HINs) are a kind of graphical model for integrating and modeling real-world information. Through HINs, we can integrate and model the large-scale heterogeneous TCM data into structured graph data and use this as a basis for analysis.Methods: Mining categorizations from TCM data is an important task for precision medicine. In this paper, we propose a novel structured learning model to solve the problem of formula regularity, a pivotal task in prescription optimization. We integrate clustering with ranking in a heterogeneous information network.Results: The results from experiments on the Pharmacopoeia of the People's Republic of China (ChP) demonstrate the effectiveness and accuracy of the proposed model for discovering useful categorizations of formulas.Conclusions: We use heterogeneous information networks to model TCM data and propose a TCM-HIN. Combining the heterogeneous graph with the probability graph, we proposed the TCM-Clus algorithm, which combines clustering with ranking and classifies traditional Chinese medicine prescriptions. The results of the categorizations can help Chinese medicine practitioners to make clinical decision. [ABSTRACT FROM AUTHOR]

Background: As global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform's effectiveness in enhancing doctors' diagnostic efficiency and accuracy. Methods: In this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors. Results: The use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors. Conclusion: The diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Background: Handheld computers for data collection (HCDC) and management have become increasingly common in health research. However, current knowledge about the use of HCDC in health research in China is very limited. In this study, we administered a survey to a hard-to-reach population in China using HCDC and assessed the acceptability and adoption of HCDC in China. Methods: Handheld computers operating Windows Mobile and Questionnaire Development Studio (QDS) software (Nova Research Company) were used for this survey. Questions on tobacco use and susceptibility were drawn from the Global Adult Tobacco Survey (GATS) and other validated instruments, and these were programmed in Chinese characters by local staff. We conducted a half-day training session for survey supervisors and a three-day training session for 20 interviewers and 9 supervisors. After the training, all trainees completed a self-assessment of their skill level using HCDC. The main study was implemented in fall 2010 in 10 sites, with data managed centrally in Beijing. Study interviewers completed a post-survey evaluation questionnaire on the acceptability and utility of HCDC in survey research. Results: Twenty-nine trainees completed post-training surveys, and 20 interviewers completed post-data collection questionnaires. After training, more than 90% felt confident about their ability to collect survey data using HCDC, to transfer study data from a handheld computer to a laptop, and to encrypt the survey data file. After data collection, 80% of the interviewers thought data collection and management were easy and 60% of staff felt confident they could solve problems they might encounter. Overall, after data collection, nearly 70% of interviewers reported that they would prefer to use handheld computers for future surveys. More than half (55%) felt the HCDC was a particularly useful data collection tool for studies conducted in China. Conclusions: We successfully conducted a health-related survey using HCDC. Using handheld computers for data collection was a feasible, acceptable, and preferred method by Chinese interviewers. Despite minor technical issues that occurred during data collection, HCDC is a promising methodology to be used in survey-based research in China. [ABSTRACT FROM AUTHOR]

Background: It has been shown that the entities in everyday clinical text are often expressed in a way that varies from how they are expressed in the nomenclature. Owing to lots of synonyms, abbreviations, medical jargons or even misspellings in the daily used physician notes in clinical information system (CIS), the terminology without enough synonyms may not be adequately suitable for the task of Chinese clinical term recognition.Methods: This paper demonstrates a validated system to retrieve the Chinese term of clinical finding (CTCF) from CIS and map them to the corresponding concepts of international clinical nomenclature, such as SNOMED CT. The system focuses on the SNOMED CT with Chinese synonyms enrichment (SCCSE). The literal similarity and the diagnosis-related similarity metrics were used for concept mapping. Two CTCF recognition methods, the rule- and terminology-based approach (RTBA) and the conditional random field machine learner (CRF), were adopted to identify the concepts in physician notes. The system was validated against the history of present illness annotated by clinical experts. The RTBA and CRF could be combined to predict new CTCFs besides SCCSE persistently.Results: Around 59,000 CTCF candidates were accepted as valid and 39,000 of them occurred at least once in the history of present illness. 3,729 of them were accordant with the description in referenced Chinese clinical nomenclature, which could cross map to other international nomenclature such as SNOMED CT. With the hybrid similarity metrics, another 7,454 valid CTCFs (synonyms) were succeeded in concept mapping. For CTCF recognition in physician notes, a series of experiments were performed to find out the best CRF feature set, which gained an F-score of 0.887. The RTBA achieved a better F-score of 0.919 by the CTCF dictionary created in this research.Conclusions: This research demonstrated that it is feasible to help the SNOMED CT with Chinese synonyms enrichment based on physician notes in CIS. With continuous maintenance of SCCSE, the CTCFs could be precisely retrieved from free text, and the CTCFs arranged in semantic hierarchy of SNOMED CT could greatly improve the meaningful use of electronic health record in China. The methodology is also useful for clinical synonyms enrichment in other languages. [ABSTRACT FROM AUTHOR]

Background: Healthcare providers generate a huge amount of biomedical data stored in either legacy system (paper-based) format or electronic medical records (EMR) around the world, which are collectively referred to as big biomedical data (BBD). To realize the promise of BBD for clinical use and research, it is an essential step to extract key data elements from unstructured medical records into patient-centered electronic health records with computable data elements. Our objective is to introduce a novel solution, known as a double-reading/entry system (DRESS), for extracting clinical data from unstructured medical records (MR) and creating a semi-structured electronic health record database, as well as to demonstrate its reproducibility empirically.Methods: Utilizing the modern cloud-based technologies, we have developed a comprehensive system that includes multiple subsystems, from capturing MRs in clinics, to securely transferring MRs, storing and managing cloud-based MRs, to facilitating both machine learning and manual reading, and to performing iterative quality control before committing the semi-structured data into the desired database. To evaluate the reproducibility of extracted medical data elements by DRESS, we conduct a blinded reproducibility study, with 100 MRs from patients who have undergone surgical treatment of lung cancer in China. The study uses Kappa statistic to measure concordance of discrete variables, and uses correlation coefficient to measure reproducibility of continuous variables.Results: Using the DRESS, we have demonstrated the feasibility of extracting clinical data from unstructured MRs to create semi-structured and patient-centered electronic health record database. The reproducibility study with 100 patient's MRs has shown an overall high reproducibility of 98 %, and varies across six modules (pathology, Radio/chemo therapy, clinical examination, surgery information, medical image and general patient information).Conclusions: DRESS uses a double-reading, double-entry, and an independent adjudication, to manually curate structured data elements from unstructured clinical data. Further, through distributed computing strategies, DRESS protects data privacy by dividing MR data into de-identified modules. Finally, through internet-based computing cloud, DRESS enables many data specialists to work in a virtual environment to achieve the necessary scale of processing thousands MRs within days. This hybrid system represents probably a workable solution to solve the big medical data challenge. [ABSTRACT FROM AUTHOR]

Background: The probability of heart failure during the perioperative period is 2% on average and it is as high as 17% when accompanied by cardiovascular diseases in China. It has been the most significant cause of postoperative death of patients. However, the patient is managed by the flow of information during the operation, but a lot of clinical information can make it difficult for medical staff to identify the information relevant to patient care. There are major practical and technical barriers to understand perioperative complications.Methods: In this work, we present three machine learning methods to estimate risks of heart failure, which extract intraoperative vital signs monitoring data into different modal representations (statistical learning representation, text learning representation, image learning representation). Firstly, we extracted features of vital signs monitoring data of surgical patients by statistical analysis. Secondly, the vital signs data is converted into text information by Piecewise Approximate Aggregation (PAA) and Symbolic Aggregate Approximation (SAX), then Latent Dirichlet Allocation (LDA) model is used to extract text topics of patients for heart failure prediction. Thirdly, the vital sign monitoring time series data of the surgical patient is converted into a grid image by using the grid representation, and then the convolutional neural network is directly used to identify the grid image for heart failure prediction. We evaluated the proposed methods in the monitoring data of real patients during the perioperative period.Results: In this paper, the results of our experiment demonstrate the Gradient Boosting Decision Tree (GBDT) classifier achieves the best results in the prediction of heart failure by statistical feature representation. The sensitivity, specificity and the area under the curve (AUC) of the best method can reach 83, 85 and 84% respectively.Conclusions: The experimental results demonstrate that representation learning model of vital signs monitoring data of intraoperative patients can effectively capture the physiological characteristics of postoperative heart failure. [ABSTRACT FROM AUTHOR]

Background: Acute kidney injury (AKI) after coronary artery bypass grafting (CABG) surgery is associated with poor outcomes. The objective of this study was to apply a new machine learning (ML) method to establish prediction models of AKI after CABG. Methods: A total of 2,780 patients from two medical centers in East China who underwent primary isolated CABG were enrolled. The dataset was randomly divided for model training (80%) and model testing (20%). Four ML models based on LightGBM, Support vector machine (SVM), Softmax and random forest (RF) algorithms respectively were established in Python. A total of 2,051 patients from two other medical centers were assigned to an external validation group to verify the performances of the ML prediction models. The models were evaluated using the area under the receiver operating characteristics curve (AUC), Hosmer-Lemeshow goodness-of-fit statistic, Bland-Altman plots, and decision curve analysis. The outcome of the LightGBM model was interpreted using SHapley Additive exPlanations (SHAP). Results: The incidence of postoperative AKI in the modeling group was 13.4%. Similarly, the incidence of postoperative AKI of the two medical centers in the external validation group was 8.2% and 13.6% respectively. LightGBM performed the best in predicting, with an AUC of 0.8027 in internal validation group and 0.8798 and 0.7801 in the external validation group. The SHAP revealed the top 20 predictors of postoperative AKI ranked according to the importance, and the top three features on prediction were the serum creatinine in the first 24 h after operation, the last preoperative Scr level, and body surface area. Conclusion: This study provides a LightGBM predictive model that can make accurate predictions for AKI after CABG surgery. The LightGBM model shows good predictive ability in both internal and external validation. It can help cardiac surgeons identify high-risk patients who may experience AKI after CABG surgery. [ABSTRACT FROM AUTHOR]

Background: In the modern era of antibiotics, healthcare-associated infections (HAIs) have emerged as a prominent and concerning health threat worldwide. Implementing an electronic surveillance system for healthcare-associated infections offers the potential to not only alleviate the manual workload of clinical physicians in surveillance and reporting but also enhance patient safety and the overall quality of medical care. Despite the widespread adoption of healthcare-associated infections surveillance systems in numerous hospitals across China, several challenges persist. These encompass incomplete coverage of all infection types in the surveillance, lack of clarity in the alerting results provided by the system, and discrepancies in sensitivity and specificity that fall short of practical expectations. Methods: We design and develop a knowledge-based healthcare-associated infections surveillance system (KBHAIS) with the primary goal of supporting clinicians in their surveillance of HAIs. The system operates by automatically extracting infection factors from both structured and unstructured electronic health data. Each patient visit is represented as a tuple list, which is then processed by the rule engine within KBHAIS. As a result, the system generates comprehensive warning results, encompassing infection site, infection diagnoses, infection time, and infection probability. These knowledge rules utilized by the rule engine are derived from infection-related clinical guidelines and the collective expertise of domain experts. Results: We develop and evaluate our KBHAIS on a dataset of 106,769 samples collected from 84,839 patients at Gansu Provincial Hospital in China. The experimental results reveal that the system achieves a sensitivity rate surpassing 0.83, offering compelling evidence of its effectiveness and reliability. Conclusions: Our healthcare-associated infections surveillance system demonstrates its effectiveness in promptly alerting patients to healthcare-associated infections. Consequently, our system holds the potential to considerably diminish the occurrence of delayed and missed reporting of such infections, thereby bolstering patient safety and elevating the overall quality of healthcare delivery. [ABSTRACT FROM AUTHOR]

The Chinese government relaxed the Zero-COVID policy on Dec 15, 2022, and reopened the border on Jan 8, 2023. Therefore, COVID prevention in China is facing new challenges. Though there are plenty of prior studies on COVID, none is regarding the predictions on daily confirmed cases, and medical resources needs after China reopens its borders. To fill this gap, this study innovates a combination of the Erdos Renyl network, modified computational model SEIRS , and python code instead of only mathematical formulas or computer simulations in the previous studies. The research background in this study is Shanghai, a representative city in China. Therefore, the results in this study also demonstrate the situation in other regions of China. According to the population distribution and migration characteristics, we divided Shanghai into six epidemic research areas. We built a COVID spread model of the Erodos Renyl network. And then, we use python code to simulate COVID spread based on modified SEIRS model. The results demonstrate that the second and third waves will occur in July–September and Oct-Dec, respectively. At the peak of the epidemic in 2023, the daily confirmed cases will be 340,000, and the cumulative death will be about 31,500. Moreover, 74,000 hospital beds and 3,700 Intensive Care Unit (ICU) beds will be occupied in Shanghai. Therefore, Shanghai faces a shortage of medical resources. In this simulation, daily confirmed cases predictions significantly rely on transmission, migration, and waning immunity rate. The study builds a mixed-effect model to verify further the three parameters' effect on the new confirmed cases. The results demonstrate that migration and waning immunity rates are two significant parameters in COVID spread and daily confirmed cases. This study offers theoretical evidence for the government to prevent COVID after China opened its borders. [ABSTRACT FROM AUTHOR]

Background: Accurately predicting the risk of atherosclerotic cardiovascular disease (ASCVD) is crucial for implementing individualized prevention strategies and improving patient outcomes. Our objective is to develop machine learning (ML)-based models for predicting ASCVD risk in a prospective Chinese population and compare their performance with conventional regression models. Methods: A hybrid dataset consisting of 551 features was used, including 98 demographic, behavioral, and psychological features, 444 Electrocardiograph (ECG) features, and 9 Echocardiography (Echo) features. Seven machine learning (ML)-based models were trained, validated, and tested after selecting the 30 most informative features. We compared the discrimination, calibration, net benefit, and net reclassification improvement (NRI) of the ML models with those of conventional ASCVD risk calculators, such as the Pooled Cohort Equations (PCE) and Prediction for ASCVD Risk in China (China-PAR). Results: The study included 9,609 participants (mean age 53.4 ± 10.4 years, 53.7% female), and during a median follow-up of 4.7 years, 431 (4.5%) participants developed ASCVD. In the testing set, the final ML-based ANN model outperformed PCE, China-PAR, recalibrated PCE, and recalibrated China-PAR in predicting ASCVD. This was demonstrated by the model's higher area under the curve (AUC) of 0.800, compared to 0.777, 0.780, 0.779, and 0.779 for the other models, respectively. Additionally, the model had a lower Hosmer–Lemeshow χ2 of 9.1, compared to 37.3, 67.6, 126.6, and 18.6 for the other models. The net benefit at a threshold of 5% was also higher for the ML-based ANN model at 0.017, compared to 0.016, 0.013, 0.017, and 0.016 for the other models, respectively. Furthermore, the NRI was 0.089 for the ML-based ANN model, while it was 0.355, 0.098, and 0.088 for PCE, China-PAR, and recalibrated PCE, respectively. Conclusions: Compared to conventional regression ASCVD risk calculators, such as PCE and China-PAR, the ANN prediction model may help optimize identification of individuals at heightened cardiovascular risk by flexibly incorporating a wider range of potential predictors. The findings may help guide clinical decision-making and ultimately contribute to ASCVD prevention and management. [ABSTRACT FROM AUTHOR]

Background: Intensive care information systems (ICIS) are continuously evolving to meet the ever changing information needs of intensive care units (ICUs), providing the backbone for a safe, intelligent and efficient patient care environment. Although beneficial for the international advancement in building smart environments to transform ICU services, knowledge about the contemporary development of ICIS worldwide, their usage and impacts is limited. This study aimed to fill this knowledge gap by researching the development and implementation of an ICIS in a Chinese hospital, nurses' use of the system, and the impact of system use on critical care nursing processes and outcomes.Methods: This descriptive case study was conducted in a 14-bed Respiratory ICU in a tertiary hospital in Beijing. Participative design was the method used for ICU nurses, hospital IT department and a software company to collaboratively research and develop the ICIS. Focus group discussions were conducted to understand the subjective perceptions of the nurses toward the ICIS. Nursing documentation time and quality were compared before and after system implementation. ICU nursing performance was extracted from the annual nursing performance data collected by the hospital.Results: A participative design process was followed by the nurses in the ICU, the hospital IT staff and the software engineers in the company to develop and implement a highly useful ICIS. Nursing documentation was fully digitized and was significantly improved in quality and efficiency. The wrong data, missing data items and calculation errors were significantly reduced. Nurses spent more time on direct patient care after the introduction of the ICIS. The accuracy and efficiency of medication administration was also improved. The outcome was improvement in ward nursing performance as measured by ward management, routine nursing practices, disinfection and isolation, infection rate and mortality rate.Conclusions: Nurses in this ICU unit in China actively participated in the ICIS development and fully used the system to document care. Introduction of the ICIS led to significant improvement in quality and efficiency in nursing documentation, medication order transcription and administration. It allowed nurses to spend more time with patients to improve quality of care. These led to improvement in overall nursing performance. Further study should investigate how the ICIS system contributes to the improvement in decision making of ICU nurses and intensivists. [ABSTRACT FROM AUTHOR]

Background: The incidence of stroke is a challenge in China, as stroke imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) is an essential indicator of utilization of medical services and is usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on a machine learning algorithm to predict ischemic stroke patients' LOS. Methods: A total of 18,195 ischemic stroke patients' electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. The prediction of LOS was regarded as a multi classification problem, and LOS was divided into three categories: 1–7 days, 8–14 days and more than 14 days. After preprocessing the data and feature selection, the XGBoost algorithm was used to build a machine learning model. Ten fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the XGBoost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature importance. Results: Compared with the naive Bayesian algorithm, logistic region algorithm, decision tree classifier algorithm and ADaBoost classifier algorithm, the XGBoot algorithm has higher ACC, RE and F1 measure. The average ACC, RE and F1 measure were 0.89, 0.89 and 0.89 under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features in terms of hemiplegia aphasia, MRS, NIHSS, TIA, Operation or not, coma index etc. were found to be the top features in importance in predicting the LOS of ischemic stroke patients. Conclusions: The XGBoost algorithm was an appropriate machine learning method for predicting the LOS of patients with ischemic stroke. Based on the prediction model, an intelligent medical management prediction system could be developed to predict the LOS based on ischemic stroke patients' electronic medical records. [ABSTRACT FROM AUTHOR]

In China, patients usually determine their medical specialty before they register the corresponding specialists in the hospitals. This process usually requires a lot of medical knowledge for the patients. As a result, many patients do not register the correct specialty for the first time if they do not receive help from the hospitals. In this study, we try to automatically direct the patients to the appropriate specialty based on the symptoms they described. As far as we know, this is the first study to solve the problem. We propose a neural network-based model based on a hybrid model integrated with an attention mechanism. To prove the actual effect of this hybrid model, we utilized a data set of more than 40,000 items, including eight departments, such as Otorhinolaryngology, Pediatrics, and other common departments. The experiment results show that the hybrid model achieves more than 93.5% accuracy and has a high generalization capacity, which is superior to traditional classification models. [ABSTRACT FROM AUTHOR]

Background: With the rapid development of online health communities (OHCs), an increasing number of physicians provide services in OHCs that enable patients to consult online in China. However, it is difficult for patients to figure out the professional level of doctors before consultation and diagnosis because of information asymmetry. A wealth of information about physicians is displayed in their profiles as a new way to help patients evaluate and select quickly and accurately. Objective: This research explores how the profile information (PI) presented in OHCs influences patients' impression formation, especially the perception of professional capital (i.e., status capital and decisional capital). The impression influences their intention to consult further, which is partially mediated by the initial trust. The Toulmin's model of argumentation is used to decide the strength of the argument presented in physicians' homepage information and divide it into claim, data, and backing. Methods: This study conducts an internet experiment and recruits 386 subjects through the internet to investigate the effect of impression formation on online selection behavior by a patient. Results: The results show that the strength of argument has a significant positive association with the perception of professional capital. Perceptions of professional capital are highest when a fully composed argument (claim/data/backing) is included in a profile, with claim/data being the next highest and claim only the lowest. Recommendations from connections have the strongest impact. In turn, patients' selection decisions are influenced by their perception of professional capital, which is partially mediated by initial trust. Conclusions: This study is significant in terms of its implications for theory and practice. On the one hand, this research contributes to the online health community literature and suggests that the perception of professional capital on physicians should be pre-presumed and built based on the information before in-person interaction online. On the other hand, this study is helpful in understanding the effect of various components included in PI on perceiving physicians' abilities, and not all information is equally important. [ABSTRACT FROM AUTHOR]

Background: Venous thromboembolism (VTE) risk assessment in surgical patients is important for the appropriate diagnosis and treatment of patients. The commonly used Caprini model is limited by its inadequate ability to discriminate between risk stratums on the surgical population in southwest China and lengthy risk factors. The purpose of this study was to establish an improved VTE risk assessment model that is accurate and simple.Methods: This study is based on the clinical data from 81,505 surgical patients hospitalized in the Southwest Hospital of China between January 1, 2019 and June 18, 2021. Among the population, 559 patients developed VTE. An improved VTE risk assessment model, SW-model, was established through Logistic Regression, with comparisons to both Caprini and Random Forest.Results: The SW-model incorporated eight risk factors. The area under the curve (AUC) of SW-model (0.807 [0.758, 0.853], 0.804 [0.765, 0.840]), are significantly superior (p = 0.001 and p = 0.044) to those of the Caprini (0.705 [0.652, 0.757], 0.758 [0.719, 0795]) on two test sets, but inferior (p < 0.001 and p = 0.002) to Random Forest (0.854 [0.814, 0.890], 0.839 [0.806, 0.868]). In decision curve analysis, within threshold range from 0.015 to 0.04, the DCA curves of the SW-model are superior to Caprini and two default strategies.Conclusions: The SW-model demonstrated a higher discriminative capability to distinguish VTE positive in surgical patients compared with the Caprini model. Compared to Random Forest, Logistic Regression based SW-model provided interpretability which is essential in guarantee the procedure of risk assessment transparent to clinicians. [ABSTRACT FROM AUTHOR]

Background: Since the first case of HIV infection was reported in China in 1985, the incidence and mortality of AIDS have been increasing rapidly, which has caused serious damage to the life and health of people in China and all over the world. Therefore, it is of great significance to study the technique for predicting AIDS morbidity and mortality. The purpose of this research is to explore the applicability of the mean generation function model (MGFM) in the early warning of AIDS morbidity and mortality, to predict its prevalence trend, to enrich the prediction techniques and methods of AIDS research and to provide suggestions for AIDS transmission control.Methods: In this research, the MGFM was applied to predict the incidence and mortality of AIDS in China. AIDS incidence and mortality data in China from 2008 to 2019 were used to construct the prediction model.Results: The MGFM can predict the annual incidence and mortality of AIDS. The model constructed in this research predicted that the incidence and mortality of AIDS in China will continue to increase from 2020 to 2023.Conclusion: The mean birth function model was an effective method to monitor and predict the changing trend of AIDS incidence and mortality in China. [ABSTRACT FROM AUTHOR]

Background: Electronic sources (eSources) can improve data quality and reduce clinical trial costs. Our team has developed an innovative eSource record (ESR) system in China. This study aims to evaluate the efficiency, quality, and system performance of the ESR system in data collection and data transcription.Methods: The study used time efficiency and data transcription accuracy indicators to compare the eSource and non-eSource data collection workflows in a real-world study (RWS). The two processes are traditional data collection and manual transcription (the non-eSource method) and the ESR-based source data collection and electronic transmission (the eSource method). Through the system usability scale (SUS) and other characteristic evaluation scales (system security, system compatibility, record quality), the participants' experience of using ESR was evaluated.Results: In terms of the source data collection (the total time required for writing electronic medical records (EMRs)), the ESR system can reduce the time required by 39% on average compared to the EMR system. In terms of data transcription (electronic case report form (eCRF) filling and verification), the ESR can reduce the time required by 80% compared to the non-eSource method (difference: 223 ± 21 s). The ESR accuracy in filling the eCRF field is 96.92%. The SUS score of ESR is 66.9 ± 16.7, which is at the D level and thus very close to the acceptable margin, indicating that optimization work is needed.Conclusions: This preliminary evaluation shows that in the clinical medical environment, the ESR-based eSource method can improve the efficiency of source data collection and reduce the workload required to complete data transcription. [ABSTRACT FROM AUTHOR]

Background: Online doctor-patient consultation is a new option for orthopedic patients in China to obtain a diagnosis and treatment advice. This study explores the factors associated with online consultation to formulate operational guidelines for managing online consultations in an online medical community (OMC).Methods: An empirical model was developed to identify the factors that influence online orthopedic doctor-patient consultations in an OMC while focusing on the perceived value of and perceived trust in online consultations. The moderating effects of different risk categories of orthopedic diseases were also considered. Data from 339 feedback surveys from orthopedic patients who used online consultation services and Stata software version 14.0 were used to estimate the model parameters and test the robustness of the empirical model.Results: Of those who completed the feedback surveys, 53.42% were female patients, 82.27% were between 18 and 60 years old, and 61.98% sought consultations online more than 2 times per year. Model analysis demonstrated that the regression coefficients of the perceived value of and perceived trust in online consultations are 0.489 (p < 0.01) and 0.505 (p < 0.01), respectively. The interaction coefficient between disease risk and perceived value is 0.336 (p < 0.01), and the interaction coefficient between disease risk and perceived trust is - 0.389 (p < 0.01).Conclusions: Orthopedic patients' perceived value of and perceived trust in online consultations in an OMC can significantly influence their intention to seek online disease diagnosis and treatment consultations. The effects of perceived value and perceived trust on patients' intention to consult vary significantly across different disease risk categories. Therefore, enhancing the perceived value and perceived trust of orthopedic patients is an important component of OMC operation and management. [ABSTRACT FROM AUTHOR]

Background: Neonatal jaundice may cause severe neurological damage if poorly evaluated and diagnosed when high bilirubin occurs. The study explored how to effectively integrate high-dimensional genetic features into predicting neonatal jaundice.Methods: This study recruited 984 neonates from the Suzhou Municipal Central Hospital in China, and applied an ensemble learning approach to enhance the prediction of high-dimensional genetic features and clinical risk factors (CRF) for physiological neonatal jaundice of full-term newborns within 1-week after birth. Further, sigmoid recalibration was applied for validating the reliability of our methods.Results: The maximum accuracy of prediction reached 79.5% Area Under Curve (AUC) by CRF and could be marginally improved by 3.5% by including genetic variant (GV). Feature importance illustrated that 36 GVs contributed 55.5% in predicting neonatal jaundice in terms of gain from splits. Further analysis revealed that the main contribution of GV was to reduce the false-positive rate, i.e., to increase the specificity in the prediction.Conclusions: Our study shed light on the theoretical and practical value of GV in the prediction of neonatal jaundice. [ABSTRACT FROM AUTHOR]

Background: Diagnosis-related groups (DRGs) are a payment system that could effectively solve the problem of excessive increases in healthcare costs which are applied as a principal measure in the healthcare reform in China. However, expert-oriented DRG grouping is a black box with the drawbacks of upcoding and high cost.Methods: This study proposes a method of data-based grouping, designed and updated by machine learning algorithms, which could be trained by real cases, or even simulated cases. It inherits the decision-making rules from the expert-oriented grouping and improves performance by incorporating continuous updates at low cost. Five typical classification algorithms were assessed and some suggestions were made for algorithm choice. The kappa coefficients were reported to evaluate the performance of grouping.Results: Based on tenfold cross-validation, experiments showed that data-based grouping had a similar classification performance to the expert-oriented grouping when choosing suitable algorithms. The groupings trained by simulated cases had less accuracy when they were tested by the real cases rather than simulated cases, but the kappa coefficients of the best model were still higher than 0.6. When the grouping was tested in a new DRGs system, the average kappa coefficients were significantly improved from 0.1534 to 0.6435 by the update; and with enough computation resources, the update process could be completed in a very short time.Conclusions: As a new potential option, the data-based grouping meets the requirements of the DRGs system and has the advantages of high transparency and low cost in the design and update process. [ABSTRACT FROM AUTHOR]

Background: Computed tomography (CT) reports record a large volume of valuable information about patients' conditions and the interpretations of radiology images from radiologists, which can be used for clinical decision-making and further academic study. However, the free-text nature of clinical reports is a critical barrier to use this data more effectively. In this study, we investigate a novel deep learning method to extract entities from Chinese CT reports for lung cancer screening and TNM staging.Methods: The proposed approach presents a new named entity recognition algorithm, namely the BERT-based-BiLSTM-Transformer network (BERT-BTN) with pre-training, to extract clinical entities for lung cancer screening and staging. Specifically, instead of traditional word embedding methods, BERT is applied to learn the deep semantic representations of characters. Following the long short-term memory layer, a Transformer layer is added to capture the global dependencies between characters. Besides, pre-training technique is employed to alleviate the problem of insufficient labeled data.Results: We verify the effectiveness of the proposed approach on a clinical dataset containing 359 CT reports collected from the Department of Thoracic Surgery II of Peking University Cancer Hospital. The experimental results show that the proposed approach achieves an 85.96% macro-F1 score under exact match scheme, which improves the performance by 1.38%, 1.84%, 3.81%,4.29%,5.12%,5.29% and 8.84% compared to BERT-BTN, BERT-LSTM, BERT-fine-tune, BERT-Transformer, FastText-BTN, FastText-BiLSTM and FastText-Transformer, respectively.Conclusions: In this study, we developed a novel deep learning method, i.e., BERT-BTN with pre-training, to extract the clinical entities from Chinese CT reports. The experimental results indicate that the proposed approach can efficiently recognize various clinical entities about lung cancer screening and staging, which shows the potential for further clinical decision-making and academic research. [ABSTRACT FROM AUTHOR]

Background: Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data.Methods: An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories.Results: Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement.Conclusions: A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance. [ABSTRACT FROM AUTHOR]

Background: Online health communities (OHCs), with a wealth of multi-source information exchange, have provided a convenient way for people with diabetes to actively participate in their self-management and have been widely used. Information exchange assists people with diabetes with health-related decisions to actively engage in their care, and reduce the occurrence of potential complications of diabetes. However, there has been relatively little research on the information exchange behaviors and their effect on health in professional online medical platforms-OHCs.Objective: Using a social exchange theory, this study focuses on two sources of information (doctors and people with diabetes) to investigate information exchange behaviors and consequences. Moreover, we also examine moderating effects of information price as patients need to pay prices for consulting with doctors to obtain medical information on OHCs.Methods: By using the Python program, a rich dataset contained 22,746 doctor-patient dialogues from December 2017 to December 2018 is collected from the biggest OHC in China. Then the logistic and ordinal regression models are used to get empirical results.Results: We found that first information sharing from doctors and other people with diabetes can promote their information sharing behavior. Second, the moderating effects of information price are heterogeneous and change with the exchange participants. Third, rich information exchange supports self-management of people with diabetes and improves their health status.Conclusion: This study is among the first that tests the information exchange behavior and consequence for diabetes in OHCs and examines the moderating effects of the information price. The present study produces several insights, which have implications for social exchange, patient behavior, online health communities, and information technology in diabetes self-management literature. [ABSTRACT FROM AUTHOR]

Background: With the development and application of medical information system, semantic interoperability is essential for accurate and advanced health-related computing and electronic health record (EHR) information sharing. The openEHR approach can improve semantic interoperability. One key improvement of openEHR is that it allows for the use of existing archetypes. The crucial problem is how to improve the precision and resolve ambiguity in the archetype retrieval.Method: Based on the query expansion technology and Word2Vec model in Nature Language Processing (NLP), we propose to find synonyms as substitutes for original search terms in archetype retrieval. Test sets in different medical professional level are used to verify the feasibility.Result: Applying the approach to each original search term (n = 120) in test sets, a total of 69,348 substitutes were constructed. Precision at 5 (P@5) was improved by 0.767, on average. For the best result, the P@5 was up to 0.975.Conclusions: We introduce a novel approach that using NLP technology and corpus to find synonyms as substitutes for original search terms. Compared to simply mapping the element contained in openEHR to an external dictionary, this approach could greatly improve precision and resolve ambiguity in retrieval tasks. This is helpful to promote the application of openEHR and advance EHR information sharing. [ABSTRACT FROM AUTHOR]

Background: CPGs are not uniformly successful in improving care and several instances of implementation failure have been reported. Performing a comprehensive assessment of the barriers and enablers is key to developing an informed implementation strategy. Our objective was to investigate determinants of guideline implementation and explore associations of self-reported adherence to guidelines with characteristics of participants in China.Methods: This is a cross-sectional survey, using multi-stage stratified typical sampling based on China's economic regional divisions (the East, the Middle, the West and the Northeast). 2-5 provinces were selected from each region. 2-3 cities were selected in each province, and secondary and tertiary hospitals from each city were included. We developed a questionnaire underpinned by recommended methods for the design and conduct of self-administered surveys and based on conceptual framework of guideline use, in-depth related literature analysis, guideline development manuals, related behavior change theory. Finally, multivariate analyses were performed using logistic regression to produce adjusted odds ratios (OR) and 95% confidence intervals (95% CI).Results: The questionnaire consisted of four sections: knowledge of methodology for developing guidelines; barriers to accessing guideline; barriers to guideline implementation; and methods for improving guideline implementation. There were 1732 participants (87.3% response rate) from 51 hospitals. Of these, 77.2% reported to have used guidelines frequently or very frequently. The key barriers to guideline use were lack of education or training (46.2%), and overly simplistic wording or overly broad scope of recommendations (43.8%). Level of adherence to guidelines was associated with geographical regions (the northeast P < 0.001; the west P = 0.02; the middle P < 0.001 compared with the east), hospital grades (P = 0.028), length of practitioners' practice (P = 0.006), education background (Ph.D., P = 0.027; Master, P = 0.002), evidence-based medicine skills acquired in work unit (P = 0.012), and medical specialty of practitioner (General Practice, P = 0.006; Surgery, P = 0.043).Conclusion: Despite general acknowledgement of the importance of guidelines, the use of guidelines was not as frequent as might have been expected. To optimize the likelihood of adherence to guidelines, guideline implementation should follow an actively developed dissemination plan incorporating features associated with adherence in our study. [ABSTRACT FROM AUTHOR]

Background: China has had about 1.2 billion mobile-phone users, and this number continues to grow. However, mobile-health services (mHealth) are currently in the initial stage, and have not yet prevailed in China. Additionally, the prevalence of Parkinson's disease (PD) in China is 1700/100,000 (≥65 years). Indeed, these PD patients would benefit from mHealth to manage their disease. Therefore, we designed a study to determine attitudes toward smartphone applications (apps) for chronic condition self-management, and to discover the practicality of these apps among PD patients in China.Methods: We selected 204 participants with PD between 52 and 87 years old and surveyed their attitudes concerning the use of smartphone apps for chronic condition management via questionnaires.Results: Among the participants, 65.19% had smartphones. Among these smartphone users, 82.84% expressed a preference for using apps for PD management. This group tended to be younger and more frequent web users with higher education and better medication compliance, and they tended to have a longer PD course and worse conditions (P < 0.001, P = 0.001, P < 0.001, P = 0.041, P < 0.001, P = 0.013). Additionally, the willingness to apply apps for PD self-management was positively related to education (P < 0.001) and negatively related to age and PD course (P = 0.017, P < 0.001).Conclusion: In China, patients with PD have a generally positive attitude towards self-management through smartphone apps. Consequently, improving the coverage of smartphones with practical and handy apps is a promising strategy for PD self-management. [ABSTRACT FROM AUTHOR]

Background: With China experiencing unprecedented economic development and social change over the past three decades, Chinese policy makers and health care professionals have come to view mental health as an important outcome to monitor. Our study conducted an epidemiological study of psychosis in Guangdong province, with 20 million real-world follow-up records in the last decade.Methods: Data was collected from Guangdong mental health information platform from 2010 to 2019, which had standardized disease registration and follow-up management for nearly 600,000 patients with six categories of mental diseases and 400,000 patients with schizophrenia. We conducted clinical staging for the disease course of the patients and divided the data with various factors into different stages of disease. Quantitative analysis was utilized to investigate the high relevant indicators to the disease. The results were projected on geography map for regional distribution analysis.Results: The majority cases of mental disease incidence were between the age of 15 and 29, while the peak age for both male and female was between 20 to 24 years old. The disease course with the largest number of patients' cases was between 5 to 10 years. The therapeutic effect of patients gradually decreased with the development of disease course, while the risk increased with the disease course. The analysis of influencing factors showed that poor economic conditions incurred higher risk scores, and good medication adherence was effective in improving treatment outcomes. In addition, receiving good education contributed to the reduction of the risk of schizophrenia and the improvement of the efficiency of early treatment. Through the analysis of regional distribution of schizophrenia disease, developed economic conditions and favorable resource conditions could promote the reduction of disease risk, while in economically backward regions, it often accompanied with lower therapeutic effect and higher disease risk.Conclusions: Certain demographic factors had a relatively prominent impact on the therapeutic effect and risk of schizophrenia, such as high-quality medication adherence. Therapeutic effect and risk were highly correlated. Backward economic conditions often associated with poor efficacy and higher risk assessment, and the developed economy and better medical resource are beneficial for the treatment of psychotic. [ABSTRACT FROM AUTHOR]

Background: To provide satisfying answers, medical QA system has to understand the intentions of the users' questions precisely. For medical intent classification, it requires high-quality datasets to train a deep-learning approach in a supervised way. Currently, there is no public dataset for Chinese medical intent classification, and the datasets of other fields are not applicable to the medical QA system. To solve this problem, we construct a Chinese medical intent dataset (CMID) using the questions from medical QA websites. On this basis, we compare four intent classification models on CMID using a case study.Methods: The questions in CMID are obtained from several medical QA websites. The intent annotation standard is developed by the medical experts, which includes four types and 36 subtypes of users' intents. Besides the intent label, CMID also provides two types of additional information, including word segmentation and named entity. We use the crowdsourcing way to annotate the intent information for each Chinese medical question. Word segmentation and named entities are obtained using the Jieba and a well-trained Lattice-LSTM model. We loaded a Chinese medical dictionary consisting of 530,000 for word segmentation to obtain a more accurate result. We also select four popular deep learning-based models and compare their performances of intent classification on CMID.Results: The final CMID contains 12,000 Chinese medical questions and is organized in JSON format. Each question is labeled the intention, word segmentation, and named entity information. The information about question length, number of entities, and are also detailed analyzed. Among Fast Text, TextCNN, TextRNN, and TextGCN, Fast Text and TextCNN models have achieved the best results in four types and 36 subtypes intent classification, respectively.Conclusions: In this work, we provide a dataset for Chinese medical intent classification, which can be used in medical QA and related fields. We performed an intent classification task on the CMID. In addition, we also did some analysis on the content of the dataset. [ABSTRACT FROM AUTHOR]

Background: Cardiogenic stroke has increasing morbidity in China and brought economic burden to patient families. In cardiogenic stroke diagnosis, echocardiograph examination is one of the most important examinations. Sonographers will investigate patients' heart via echocardiograph, and describe them in the echocardiograph reports. In this study, we developed a machine learning model to automatically identify diagnosis evidences of cardiogenic stroke providing to neurologist for clinical decision making.Methods: We collected 4188 Chinese echocardiograph reports of 4018 patients, with average length 177 Chinese characters in free-text style. Collaborating with neurologists and sonographers, we summarized 149 phrases on diagnosis evidence of cardiogenic stroke such as "" (severe mitral stenosis), "" (aortic valve degeneration) and so on. Furthermore, we developed an annotated corpus via mapping 149 phrases to the 4188 reports. We selected 11 most frequent diagnosis evidence types such as "" (mitral stenosis) for further identifying. The generated corpus is divided into training set and testing set in the ratio of 8:2, which is used to train and validate a machine learning model to identify the evidence of cardiogenic stroke using BiLSTM-CRF algorithm.Results: Our machine learning method achieved the average performance on the diagnosis evidence identification is 98.03, 90.17 and 93.94% respectively. In addition, our method is capable to identify the novel diagnosis evidence of cardiogenic stroke description such as "-" (mitral stenosis), "" (aortic valve calcification) et al. CONCLUSIONS: In this study, we analyze the structure of the echocardiograph reports and summarized 149 phrases on diagnosis evidence of cardiogenic stroke. We use the phrases to generate an annotated corpus automatically, which greatly reduces the cost of manual annotation. The model trained based on the corpus also has a good performance on the testing set. The method of automatically identifying diagnosis evidence of cardiogenic stroke proposed in this study will be further refined in the practice. [ABSTRACT FROM AUTHOR]

Background: As a kind of widely distributed disease in China, acquired immune deficiency syndrome (AIDS) has been quickly growing each year, become a serious problem and caused serious damage to the life and health of people and the social events of China and the world because of its high fatality rate. It has been much concerned by all aspects of society. Therefore, developing early warning technology and finding the trend of early development are of quite significance to prevent and control human immunodeficiency virus (HIV)/AIDS. This study aimed to explore a suitable model for the morbidity of AIDS in China and establish a professional and feasible disease prediction model for the prevention and control works of AIDS.Methods: At present, the traditional linear model is still utilized by most scholars to predict the incidence of HIV/AIDS. In addition, some scholars may attempt to use the nonlinear prediction model. Both prediction models showed good fitting and prediction effects. In China, the incidence of AIDS presents linear and nonlinear characteristics. In this research, the nonlinear back propagation artificial neural network (BP-ANN) model and the typical auto-regressive integrated moving average (ARIMA) linear model were applied to predict the incidence of HIV/AIDS and compare their fitting effects.Results: Both models were capable of predicting the expected cases of AIDS. It was seen that ARIMA and BP-ANN models could be used to forecast the monthly incidence of HIV/AIDS, but the fitting and forecasting effects of the nonlinear BP neural network model were better than those of the traditional linear ARIMA model.Conclusions: In summary, it was further concluded that the BP-ANN model was a suitable way to monitor and predict the change trend and morbidity of AIDS in China. [ABSTRACT FROM AUTHOR]

Background: Short messages service (SMS) provides a practical medium for delivering content to address patients to adherence to self-management. The aim of study was to design some patient-centered health education messages, evaluate the feasibility of messages, and explore the effect of this model.Methods: The messages were designed by a panel of experts, and SMS Quality Evaluation Questionnaire was used to evaluate their quality. A two-arm randomized controlled trial was conducted to evaluate the effectiveness of this management model. Participants were randomly divided into an intervention group (IG) who received evaluated messages and a control group (CG) who received regular education. The primary outcomes were changes in plasma glucose and control rates, and the secondary outcomes were improvements in diet control, physical activities, weight control, etc. RESULTS: A total of 42 messages covering five main domains: health awareness, diet control, physical activities, living habits and weight control were designed, and the average scores of the messages were 8.0 (SD 0.7), 8.5 (SD 0.6), 7.9 (SD 1.0), 8.0 (SD 0.7), and 8.4 (SD 0.9), respectively. In the SMS intervention, 171 patients with an average age of 55.1 years were involved, including 86 in the CG and 85 in the IG. At 12 months, compared with the control group (CG), the decrease of fasting plasma glucose (FPG) (1.5 vs. 0.4, P = 0.011) and control rate (49.4% vs. 33.3%, P = 0.034), the postprandial glucose (PPG) (5.8 vs. 4.2, P = 0.009) and control rate (57.8% vs. 33.7%, P = 0.002) were better in the intervention group (IG). In terms of self-management, improvements in weight control (49.3% vs. 28.2%, P = 0.031), vegetables consumption (87.3% vs. 29.0%, P < 0.001), fruits consumption (27.5% vs. 7.4%, P = 0.022), and physical activities (84.7% vs. 70.0%, P = 0.036) were better in the IG than in the CG.Conclusions: The overall quality of the messages was high. It was effective and feasible to carry out an SMS intervention to improve the behavioral habits of patients with chronic diseases in remote and undeveloped areas.Trial Registration: Clinicaltrials.gov, ChiCTR1900023445. Registered May 28, 2019--Retrospectively registered. [ABSTRACT FROM AUTHOR]

Background: Obstructive sleep apnea (OSA) is a sleep disorder with a high prevalence in China. Standard diagnosis of OSA requires polysomnography (PSG). Currently, smart phone applications (apps) are widely used as an important source of health guidance. However, the quality of the information provided by these apps has not been carefully assessed.Methods: We searched for sleep apps available in China. We designed an evaluation scale that included scientific, functionality and usability, and accountability domains. The Scientific domain included an index of 16 items to evaluate the scientific quality of the apps for their level of adherence to PSG. The functionality and usability domain included 10 items to evaluate the functions of apps and 1 item to define whether the apps needed to connect with other devices. The accountability domain included 9 items that came from the Silberg Scale to evaluate whether the information provided by apps were trustable or not. We then calculated the sum of all domains. We also evaluated the popularity of each app.Results: A total of 2379 apps were found, and 127 met the inclusion criteria. The mean total score of the apps was 14.23 ± 3.93. The mean scores of scientific basis, functionality and usability, and accountability were 5.51 ± 2.58, 2.90 ± 1.84, and 2.90 ± 1.84. The scientific scores of apps that could connect to other devices were higher than those of apps that worked alone (mean score: 5.26 vs. 4.17, P < 0.001). The functionality and usability score was correlated with the accountability score, and the coefficient of correlation was 0.304 (P = 0.001).Conclusions: Apps that could connect to other devices were more scientific and powerful than those that worked alone. Multifunctional apps were more popular and reliable. Because of the low quality of sleep apps in China, more work is necessary to create an ideal app. [ABSTRACT FROM AUTHOR]

Background: Chronic periodontitis is a multifactorial polygenetic disease with an increasing number of associated factors that have been identified over recent decades. Longitudinal epidemiologic studies have demonstrated that the risk factors were related to the progression of the disease. A traditional multivariate regression model was used to find risk factors associated with chronic periodontitis. However, the approach requirement of standard statistical procedures demands individual independence. Multilevel modelling (MLM) data analysis has widely been used in recent years, regarding thorough hierarchical structuring of the data, decomposing the error terms into different levels, and providing a new analytic method and framework for solving this problem. The purpose of our study is to investigate the relationship of clinical periodontal index and the risk factors in chronic periodontitis through MLM analysis and to identify high-risk individuals in the clinical setting.Methods: Fifty-four patients with moderate to severe periodontitis were included. They were treated by means of non-surgical periodontal therapy, and then made follow-up visits regularly at 3, 6, and 12 months after therapy. Each patient answered a questionnaire survey and underwent measurement of clinical periodontal parameters.Results: Compared with baseline, probing depth (PD) and clinical attachment loss (CAL) improved significantly after non-surgical periodontal therapy with regular follow-up visits at 3, 6, and 12 months after therapy. The null model and variance component models with no independent variables included were initially obtained to investigate the variance of the PD and CAL reductions across all three levels, and they showed a statistically significant difference (P < 0.001), thus establishing that MLM data analysis was necessary. Site-level had effects on PD and CAL reduction; those variables could explain 77-78% of PD reduction and 70-80% of CAL reduction at 3, 6, and 12 months. Other levels only explain 20-30% of PD and CAL reductions. Site-level had the greatest effect on PD and CAL reduction.Conclusions: Non-surgical periodontal therapy with regular follow-up visits had a remarkable curative effect. All three levels had a substantial influence on the reduction of PD and CAL. Site-level had the largest effect on PD and CAL reductions. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular disease (CVD) has become the leading cause of death in China, and most of the cases can be prevented by controlling risk factors. The goal of this study was to build a corpus of CVD risk factor annotations based on Chinese electronic medical records (CEMRs). This corpus is intended to be used to develop a risk factor information extraction system that, in turn, can be applied as a foundation for the further study of the progress of risk factors and CVD.Results: We designed a light annotation task to capture CVD risk factors with indicators, temporal attributes and assertions that were explicitly or implicitly displayed in the records. The task included: 1) preparing data; 2) creating guidelines for capturing annotations (these were created with the help of clinicians); 3) proposing an annotation method including building the guidelines draft, training the annotators and updating the guidelines, and corpus construction. Meanwhile, we proposed some creative annotation guidelines: (1) the under-threshold medical examination values were annotated for our purpose of studying the progress of risk factors and CVD; (2) possible and negative risk factors were concerned for the same reason, and we created assertions for annotations; (3) we added four temporal attributes to CVD risk factors in CEMRs for constructing long term variations. Then, a risk factor annotated corpus based on de-identified discharge summaries and progress notes from 600 patients was developed. Built with the help of clinicians, this corpus has an inter-annotator agreement (IAA) F1-measure of 0.968, indicating a high reliability.Conclusion: To the best of our knowledge, this is the first annotated corpus concerning CVD risk factors in CEMRs and the guidelines for capturing CVD risk factor annotations from CEMRs were proposed. The obtained document-level annotations can be applied in future studies to monitor risk factors and CVD over the long term. [ABSTRACT FROM AUTHOR]

Background: The accumulation of medical documents in China has rapidly increased in the past years. We focus on developing a method that automatically performs ICD-10 code assignment to Chinese diagnoses from the electronic medical records to support the medical coding process in Chinese hospitals.Methods: We propose two encoding methods: one that directly determines the desired code (flat method), and one that hierarchically determines the most suitable code until the desired code is obtained (hierarchical method). Both methods are based on instances from the standard diagnostic library, a gold standard dataset in China. For the first time, semantic similarity estimation between Chinese words are applied in the biomedical domain with the successful implementation of knowledge-based and distributional approaches. Characteristics of the Chinese language are considered in implementing distributional semantics. We test our methods against 16,330 coding instances from our partner hospital.Results: The hierarchical method outperforms the flat method in terms of accuracy and time complexity. Representing distributional semantics using Chinese characters can achieve comparable performance to the use of Chinese words. The diagnoses in the test set can be encoded automatically with micro-averaged precision of 92.57 %, recall of 89.63 %, and F-score of 91.08 %. A sharp decrease in encoding performance is observed without semantic similarity estimation.Conclusion: The hierarchical nature of ICD-10 codes can enhance the performance of the automated code assignment. Semantic similarity estimation is demonstrated indispensable in dealing with Chinese medical text. The proposed method can greatly reduce the workload and improve the efficiency of the code assignment process in Chinese hospitals. [ABSTRACT FROM AUTHOR]

Background As a part of nationwide healthcare reforms, the Chinese government launched web-based appointment systems (WAS) to provide a solution to problems around outpatient appointments and services. These have been in place in all Chinese public tertiary hospitals since 2009. Methods Questionnaires were collected from both patients and doctors in one large tertiary public hospital in Shanghai, China.Data were analyzed to measure their satisfaction and views about the WAS. Results The 1000 outpatients randomly selected for the survey were least satisfied about the waiting time to see a doctor. Even though the WAS provided a much more convenient booking method, only 17% of patients used it. Of the 197 doctors surveyed, over 90% thought it was necessary to provide alternative forms of appointment booking systems for outpatients. However, about 80% of those doctors who were not associated professors would like to provide an 'on-the-spot' appointment option, which would lead to longer waits for patients. Conclusions Patients were least satisfied about the waiting times. To effectively reduce appointmentwaiting times is therefore an urgent issue. Despite the benefits of using the WAS, most patients still registered via the usual method of queuing, suggesting that hospitals and health service providers should promote and encourage the use of the WAS. Furthermore, Chinese health providers need to help doctors to take others' opinions or feedback into consideration when treating patients to minimize the gap between patients' and doctors' opinions. These findings may provide useful information for both practitioners and regulators, and improve recognition of this efficient and useful booking system, which may have far-reaching and positive implications for China's ongoing reforms. [ABSTRACT FROM AUTHOR]

Background: In accordance with the People's Republic of China's (China) National Health Reform Plan of 2009, two of the nation's leading hospitals, located in Beijing, have implemented electronic medical record (EMR) systems from different vendors. To inform future EMR adoption and policy in China, as well as informatics research in the US, this study compared the United State's Hospital Meaningful Use (MU) Objectives (phase 1) objectives to the EMR functionality of two early hospital EMR adopters in China. Methods: At both hospitals, the researchers observed a physician using the EMR and noted MU functionality that was seen and functionality that was not seen yet was available in the EMR. The information technology department was asked about the availability of functionality neither observed nor known to the physician. Results and conclusions: Approximately half the MU objectives were available in each EMR. Some differences between the EMRs in the study and MU objectives were attributed to operational differences between the health systems and the cultures in the two countries. [ABSTRACT FROM AUTHOR]

The article presents information on the design and implementation of the web-based Chinese Renal Data System (CNRDS). It was launched by the Chinese Society of Nephrology in April 2012. The CNRDS aims to collect structured demographic, clinical, and laboratory data for dialysis cases, along with developing a kidney disease database for researchers and policy makers.

Background: Exclusive breastfeeding for the first 6 months of life is the optimal way to feed infants. However, recent studies suggest that exclusive breastfeeding rates in China remain low and are well below the recommended target. There has been evidence that a lack of awareness of, or exposure to, breastfeeding information is associated with poor breastfeeding practices. WeChat, the most widely used social networking platform in China, has shown some potential to promote health behaviours. We thus hypothesised that a breastfeeding intervention program delivered via WeChat would achieve at least a 10% increase in exclusive breastfeeding prevalence at 6 months compared to the control group.Methods: A two-arm, parallel, multicentre randomised controlled trial of 1000 pregnant women will be conducted at four maternity hospitals of Chengdu, China. Eligible women who consent to participate in the trial will be recruited at 28-30 weeks of gestation, and randomly allocated to either the intervention group (participants receive breastfeeding-related information from WeChat) or the control group (participants receive non-breastfeeding information from WeChat) using a central randomisation system on a 1:1 ratio at each participating site. The primary outcomes are exclusive breastfeeding rate and full breastfeeding rate at 6 months postpartum. All randomised participants will be included in the outcome analyses with missing data being imputed based on the best-case and worst-case scenarios. Multilevel mixed regression models will be used in the primary analyses to assess the effectiveness of intervention program on the breastfeeding rates.Discussion: This trial uses the most widely used social media program as a means of delivering messages to mothers to increase exclusive breastfeeding in China. Increasing exclusive breastfeeding will contribute to meeting the health and environmental goals of the Sustainable Development Guidelines. Trial registration ClinicalTrials.gov, NCT04499404. Registered 5 August 2020-Retrospectively registered, https://clinicaltrials.gov/show/NCT04499404. [ABSTRACT FROM AUTHOR]

Background: Akkermansia muciniphila is an anaerobic bacterium residing in the healthy intestinal tract of host and its quantity has a negative correlation with various host diseases. This study for the first time provides a holistic bibliometric aspect of Akkermansi muciniphila research in the literature and shows the hot topic terms of these articles in any period of time.Methods: Scopus database was selected to retrieve documents relevant to Akkermansia muciniphila in any language up to 2019. The bibliometric profile of Akkermansia muciniphila articles including subject area, year distribution, citations, institutions, journals, authors, and countries was systematically characterized and the collaboration networks of authors and countries as well as the burst detection algorithm of the words in the titles, abstracts and keywords were visualized.Results: There is a progressive growing trend in research on Akkermansia from 2004 to 2019 with a total of 566 articles during this period. Out of 353 original articles, there are 194 animal studies (155 studies on mice) and 112 human studies. Also, 65 various diseases were investigated in these studies. The most focused conditions are obesity (71 articles) and type2 diabetes (39 articles). The United States is the leading country on Akkermansia publications (n = 132), followed by China (n = 95). Frontiers in Microbiology is the most dominant journal with 23 Akkermansia publications. In addition, "cancer" is the hot topic of recent Akkermansia research.Conclusion: Akkermansia research is of progressive interest during the last decade and the studies on this subject move towards its relationship with cancer and its promising effect on health. [ABSTRACT FROM AUTHOR]

Background: Differentiating between ulcerative colitis (UC), Crohn's disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases through machine learning algorithms.Methods: A total of 6399 consecutive patients (5128 UC, 875 CD and 396 ITB) who had undergone colonoscopy examinations in the Peking Union Medical College Hospital from January 2008 to November 2018 were enrolled. The input was the description of the endoscopic image in the form of free text. Word segmentation and key word filtering were conducted as data preprocessing. Random forest (RF) and convolutional neural network (CNN) approaches were applied to different disease entities. Three two-class classifiers (UC and CD, UC and ITB, and CD and ITB) and a three-class classifier (UC, CD and ITB) were built.Results: The classifiers built in this research performed well, and the CNN had better performance in general. The RF sensitivities/specificities of UC-CD, UC-ITB, and CD-ITB were 0.89/0.84, 0.83/0.82, and 0.72/0.77, respectively, while the values for the CNN of CD-ITB were 0.90/0.77. The precisions/recalls of UC-CD-ITB when employing RF were 0.97/0.97, 0.65/0.53, and 0.68/0.76, respectively, and when employing the CNN were 0.99/0.97, 0.87/0.83, and 0.52/0.81, respectively.Conclusions: Classifiers built by RF and CNN approaches had excellent performance when classifying UC with CD or ITB. For the differentiation of CD and ITB, high specificity and sensitivity were achieved as well. Artificial intelligence through machine learning is very promising in helping unexperienced endoscopists differentiate inflammatory intestinal diseases.Conference: The abstract of this article has won the first prize of the Young Investigator Award during the Asian Pacific Digestive Week (APDW) 2019 held in Kolkata, India. [ABSTRACT FROM AUTHOR]

Background: With the development of information technology, an increasing number of healthcare professionals are using smartphones and mobile medical applications (apps) in their clinical practice. The objective of this study was to survey the use of smartphone-based medical apps among dentists in China and determine dentists' perceptions of such apps.Methods: All data were collected using anonymous questionnaires. The questionnaires for this cross-sectional study were randomly sent to dentists by email, and 379 dentists responded. Dentists' demographics and perceptions of WeChat, QQ (the most popular social media apps in China) and other medical apps were assessed; the questionnaire including questions on the purpose, frequency, daily use, and opinion of the apps they used. Questions were answered using a Likert scale (1 = strongly agree, 2 = agree, 3 = not sure, 4 = disagree, and 5 = strongly disagree).Results: A total of 379 valid responses were received; the respondents had a median age of 33.6 years old (63.3% female). All subjects (100%) owned a smartphone, and all of them installed and used WeChat or QQ in their clinical practice. Only 76% of subjects installed medical apps (other than WeChat and QQ) on their smartphones. Male dentists were more likely to install medical apps than female dentists (p < 0.05). With increasing age, the percentage of dentists who installed medical apps decreased (p < 0.001). The frequency and daily use were higher for WeChat and QQ than for medical apps. Medical apps were positively perceived, with dentists reporting that they recommend these medical apps to their peers (Likert score: 1.67 ± 0.68).Conclusion: Medical apps were perceived to have a positive impact on clinical practice, education and patient care in dentistry by providing relevant medical information. However, there will still be much room for improvement in the future. [ABSTRACT FROM AUTHOR]