Search

We present the results of the user studies of the CareMe prototype telecare service for supporting older adults with remote telecare services. The CareMe solution targets older adults who are capable of living independently and outside of a dedicated medical institution or a nursing home, but require occasional medical attention, and formal or informal care. The studies were conducted between November 2013 and March 2014 and followed the participatory design approach. The research questions focused on understanding the difference in the participatory design approach when older adults use paper prototypes vs interactive prototypes, and the difference when older adults use interactive prototypes with poor interface design vs interactive prototypes with rich interface design. In total, 45 older adults living in Slovenia participated in the field trial. They were retired, 32 of whom were nursing home residents and 13 were living independently at home. The study sample's age distribution was between 64 and 91 years. The analysis of the filed trial results indicated the importance of the factors that influence users' decision about how and when they use new technology, i.e., the perceived usefulness and the perceived ease-of-use. Main insights gained from the studies show, that it is very difficult to keep older people focused on the topics of discussion and that they have often difficulties to clearly present/express their ideas. Furthermore, abstract concepts, such as paper only prototypes proved to be challenging to cope with, whereas using real applications and services when trying out scenarios using interactive prototypes provided much better experience and feedback. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to review and analysis of the state of the art regarding triage applications (apps) for health emergencies. This research is based on a systematic review of the literature in scientific databases from 2010 to early 2021, following a prism methodology. In addition, a Google Play Store search of the triage apps found in the literature was performed for further evaluation. A total of 26 relevant papers were obtained for this study, of which 13 apps were identified. After searching for each of these apps in the Google Play Store platform, only 2 of them were obtained, and these were subsequently evaluated together with another app obtained from the link provided in the corresponding paper. In the analysis carried out, it was detected that from 2019 onwards there has been an increase in research interest in this area, since the papers obtained from this year onwards represent 38.5% of the relevant papers. This increase may be caused by the need for early selection of the most serious patients in such difficult times for the health service. According to the review carried out, an increase in mobile app research focused on Emergency Triage and a decrease in app studies for triage catastrophe have been identified. In this study it was also observed that despite the existence of many researches in this sense, only 3 apps contained in them are accessible. "TRIAGIST" does not allow the entry of an unidentified user, "Major Trauma Triage Tool" presents negative comments from users who have used it and "ESITriage" lacks updates to improve its performance. [ABSTRACT FROM AUTHOR]

The release of smartphones and tablets, which offer more advanced communication and computing capabilities, has led to the strong emergence of mHealth on the market. mHealth systems are being used to improve patients' lives and their health, in addition to facilitating communication between doctors and patients. Researchers are now proposing mHealth applications for many health conditions such as dementia, autism, dysarthria, Parkinson's disease, and so on. Usability becomes a key factor in the adoption of these applications, which are often used by people who have problems when using mobile devices and who have a limited experience of technology. The aim of this paper is to investigate the empirical usability evaluation processes described in a total of 22 selected studies related to mHealth applications by means of a Systematic Literature Review. Our results show that the empirical evaluation methods employed as regards usability could be improved by the adoption of automated mechanisms. The evaluation processes should also be revised to combine more than one method. This paper will help researchers and developers to create more usable applications. Our study demonstrates the importance of adapting health applications to users' need. [ABSTRACT FROM AUTHOR]

The aim of the study is to assess the feasibility of Drugs Rational Usage Guideline System (DRUGS)-supported clinical pathway (CP) for breast carcinoma, cataract, inguinal hernia and 2-diabetes mellitus whether the application of such a system could improve work efficiency, medical safety, and decrease hospital cost. Four kinds of diseases which included 1773 cases (where 901 cases using paper-based clinical pathways and 872 cases using DRUGS-supported clinical pathways) were selected and their demographic and clinical data were collected. The evaluation criteria were length of stay, preoperative length of stay, hospital cost, antibiotics prescribed during hospitalization, unscheduled surgery, complications and prognosis. The median total LOS was 1 to 3 days shorter in the DRUGS-supported CP group as compared to the Paper-based CP group for all types ( p < 0.05). Totel hospital cost decreased significantly in the DRUGS-supported CP group than that in Paper-based CP group. About antibiotics prescribed during hospitalization, there were no statistically differences in the time of initial dose of antibiotic and the duration of administration except the choice of antibiotic categories. The proportion of DRUGS-supported clinical pathway conditions where a broad-spectrum antibiotic was prescribed decreased from 63.6 to 34.5 % ( p < 0.01) in the Paper-based group. While after the intervention, the differences were statistically not significant in unscheduled surgery, complications and prognosis. In this study, DRUGS-supported clinical pathway for breast carcinoma, cataract, inguinal hernia, 2-diabetes mellitus was smoothly shifted from a paper-based to an electronic system, and confer benefits at the hospital level. [ABSTRACT FROM AUTHOR]

In clinical practice, many drug therapies are associated with prolongation of the QT interval. In literature, estimation of the risk of prescribing drug-induced QT prolongation is mainly executed by means of logistic regression; only one paper reported the use of machine learning techniques. In this paper, we compare the performance of both techniques on the same dataset. High risk for QT prolongation was defined as having a corrected QT interval (QTc) ≥ 450 ms or ≥ 470 ms for respectively male and female patients. Both conventional statistical methods (CSM) and machine learning techniques (MLT) were used. All algorithms were validated internally and with a hold-out dataset of respectively 512 and 102 drug-drug interactions with possible drug-induced QTc prolongation. MLT outperformed the best CSM in both internal and hold-out validation. Random forest and Adaboost classification performed best in the hold-out set with an equal harmonic mean of sensitivity and specificity (HMSS) of 81.2% and an equal accuracy of 82.4% in a hold-out dataset. Sensitivity and specificity were both high (respectively 75.6% and 87.7%). The most important features were baseline QTc value, C-reactive protein level, heart rate at baseline, age, calcium level, renal function, serum potassium level and the atrial fibrillation status. All CSM performed similarly with HMSS varying between 60.3% and 66.3%. The overall performance of logistic regression was 62.0%. MLT (bagging and boosting) outperform CSM in predicting drug-induced QTc prolongation. Additionally, 19.2% was gained in terms of performance by random forest and Adaboost classification compared to logistic regression (the most used technique in literature in estimating the risk for QTc prolongation). Future research should focus on testing the classification on fully external data, further exploring potential of other (new) machine and deep learning models and on generating data pipelines to automatically feed the data to the classifier used. [ABSTRACT FROM AUTHOR]

Emergency departments (EDs) have a key role in the public health system. They are facing a constant growth of their volume. Forecasting the daily volume is a major tool to adapt the allocation of resources. In this paper, we focus on pediatric EDs. They are specific by their strong seasonal variation, determined by the academic pace. The main contribution of this paper is to integrate the effects of this pace to the annual seasonality. We also tried out to improve the daily forecasting by forecasting the week means of the flow first. We trained and tested these models specifically on the pediatric EDs of Paris university hospital trust. For the eight pediatric EDs gathered, on average for the years 2016 to 2019, we forecasted the daily volume with a Mean Absolute Percentage Error (MAPE) of 6.6% for a 7-days forecasting, 7.1% for a 14-days forecasting and 7.6% for a 28-days forecasting. Account of rhythm allows a performance increase, with results respectively 7%, 10.1% and 8.4% better relatively to a baseline model based on a periodic regression on the weeks. [ABSTRACT FROM AUTHOR]

Precise segmentation of retinal vessels is crucial for the prevention and diagnosis of ophthalmic diseases. In recent years, deep learning has shown outstanding performance in retinal vessel segmentation. Many scholars are dedicated to studying retinal vessel segmentation methods based on color fundus images, but the amount of research works on Scanning Laser Ophthalmoscopy (SLO) images is very scarce. In addition, existing SLO image segmentation methods still have difficulty in balancing accuracy and model parameters. This paper proposes a SLO image segmentation model based on lightweight U-Net architecture called MBRNet, which solves the problems in the current research through Multi-scale Bottleneck Residual (MBR) module and attention mechanism. Concretely speaking, the MBR module expands the receptive field of the model at a relatively low computational cost and retains more detailed information. Attention Gate (AG) module alleviates the disturbance of noise so that the network can concentrate on vascular characteristics. Experimental results on two public SLO datasets demonstrate that by comparison to existing methods, the MBRNet has better segmentation performance with relatively few parameters. [ABSTRACT FROM AUTHOR]

The main goal of this article is to identify the main dimensions of a model proposal for increasing the potential of big data research in Healthcare for medical doctors' (MDs') learning, which appears as a major issue in continuous medical education and learning. The paper employs a systematic literature review of main scientific databases (PubMed and Google Scholar), using the VOSviewer software tool, which enables the visualization of scientific landscapes. The analysis includes a co-authorship data analysis as well as the co-occurrence of terms and keywords. The results lead to the construction of the learning model proposed, which includes four health big data key areas for MDs' learning: 1) data transformation is related to the learning that occurs through medical systems; 2) health intelligence includes the learning regarding health innovation based on predictions and forecasting processes; 3) data leveraging regards the learning about patient information; and 4) the learning process is related to clinical decision-making, focused on disease diagnosis and methods to improve treatments. Practical models gathered from the scientific databases can boost the learning process and revolutionise the medical industry, as they store the most recent knowledge and innovative research. [ABSTRACT FROM AUTHOR]

Coded data are the basis of information systems in all countries that rely on Diagnosis Related Groups in order to reimburse/finance hospitals, including both administrative and clinical data. To identify the problems and barriers that affect the quality of the coded data is paramount to improve data quality as well as to enhance its usability and outcomes. This study aims to explore problems and possible solutions associated with the clinical coding process. Problems were identified according to the perspective of ten medical coders, as the result of four focus groups sessions. This convenience sample was sourced from four public hospitals in Portugal. Questions relating to problems with the coding process were developed from the literature and authors' expertise. Focus groups sessions were taped, transcribed and analyzed to elicit themes. Variability in the documents used for coding, illegibility of hand writing when coding on paper, increase of errors due to an extra actor in the coding process when transcribed from paper, difficulties in the diagnoses' coding, coding delay and unavailability of resources and tools designed to help coders, were some of the problems identified. Some problems were identified and solutions such as the standardization of the documents used for coding an episode, the adoption of the electronic coding, the development of tools to help coding and audits, and the recognition of the importance of coding by the management were described as relevant factors for the improvement of the quality of data. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) analysis has been widely used in the diagnosis of stroke diseases for its low cost and noninvasive characteristics. In order to classify the EEG signals of stroke patients with cerebral infarction and cerebral hemorrhage, this paper proposes a novel EEG stroke signal classification method. This method has two highlights. The first is that a multi-feature fusion method is given by combining wavelet packet energy, fuzzy entropy and hierarchical theory. The second highlight is that a suitable classification model based on ensemble classifier is constructed for perfectly classification stroke signals. Entropy is an accessible way to measure information and uncertainty of time series. Many entropy-based methods have been developed these years. By comparing with the performances of permutation entropy, sample entropy, approximate entropy in measuring the characteristic of stroke patient's EEG signals, it can be found that fuzzy entropy has best performance in characterization stroke EEG signal. By combining hierarchical theory, wavelet packet energy and fuzzy entropy, a multi-feature fusion method is proposed. The method first calculates wavelet packet energy of EEG stroke signal, then extracts hierarchical fuzzy entropy feature by combining hierarchical theory and fuzzy entropy. The experimental results show that, compared with the fuzzy entropy feature, the classification accuracy based on the fusion feature of wavelet packet energy and hierarchical fuzzy entropy is much higher than benchmark methods. It means that the proposed multi-feature fusion method based on stroke EEG signal is an efficient measure in classifying ischemic and hemorrhagic stroke. Support vector machine (SVM), decision tree and random forest are further used as the stroke signal classification models for classifying ischemic stroke and hemorrhagic stroke. Experimental results show that, based on the proposed multi-feature fusion method, the ensemble method of random forest can get the best classification performance in accuracy among three models. [ABSTRACT FROM AUTHOR]

Precise neurosurgical guidance is critical for successful brain surgeries and plays a vital role in all phases of image-guided neurosurgery (IGN). Neuronavigation software enables real-time tracking of surgical tools, ensuring their presentation with high precision in relation to a virtual patient model. Therefore, this work focuses on the development of a novel multimodal IGN system, leveraging deep learning and explainable AI to enhance brain tumor surgery outcomes. The study establishes the clinical and technical requirements of the system for brain tumor surgeries. NeuroIGN adopts a modular architecture, including brain tumor segmentation, patient registration, and explainable output prediction, and integrates open-source packages into an interactive neuronavigational display. The NeuroIGN system components underwent validation and evaluation in both laboratory and simulated operating room (OR) settings. Experimental results demonstrated its accuracy in tumor segmentation and the success of ExplainAI in increasing the trust of medical professionals in deep learning. The proposed system was successfully assembled and set up within 11 min in a pre-clinical OR setting with a tracking accuracy of 0.5 (± 0.1) mm. NeuroIGN was also evaluated as highly useful, with a high frame rate (19 FPS) and real-time ultrasound imaging capabilities. In conclusion, this paper describes not only the development of an open-source multimodal IGN system but also demonstrates the innovative application of deep learning and explainable AI algorithms in enhancing neuronavigation for brain tumor surgeries. By seamlessly integrating pre- and intra-operative patient image data with cutting-edge interventional devices, our experiments underscore the potential for deep learning models to improve the surgical treatment of brain tumors and long-term post-operative outcomes. [ABSTRACT FROM AUTHOR]

Background: HIV treatment prescription is a complex process. Clinical decision support systems (CDSS) are a category of health information technologies that can assist clinicians to choose optimal treatments based on clinical trials and expert knowledge. The usability of some CDSSs for HIV treatment would be significantly improved by using the knowledge obtained by treating other patients. This knowledge, however, is mainly contained in patient records, whose usage is restricted due to privacy and confidentiality constraints. Methods: A treatment effectiveness measure, containing valuable information for HIV treatment prescription, was defined and a method to extract this measure from patient records was developed. This method uses an advanced cryptographic technology, known as secure Multiparty Computation (henceforth referred to as MPC), to preserve the privacy of the patient records and the confidentiality of the clinicians' decisions. Findings: Our solution enables to compute an effectiveness measure of an HIV treatment, the average time-to-treatment-failure, while preserving privacy. Experimental results show that our solution, although at proof-of-concept stage, has good efficiency and provides a result to a query within 24 min for a dataset of realistic size. Interpretation: This paper presents a novel and efficient approach HIV clinical decision support systems, that harnesses the potential and insights acquired from treatment data, while preserving the privacy of patient records and the confidentiality of clinician decisions. [ABSTRACT FROM AUTHOR]

The paper presents the mathematical modeling along with an experimental approach for the identification of arterial occlusion condition. Arterial occlusion occurs due to development of constriction and/or thrombus in the lumen of the artery. A geometry of the thrombus has been modeled for the analysis of the arterial occlusion condition in modeling part. The proposed model of the thrombus has been simulated using Comsol® multiphysics considering different hemodynamic parameters. Variation in regional oxygen saturation (rSO2) of the arterial occlusion condition has been validated with experimental process, where occlusion of the artery has been done by applying external pressure. The experimental study has been conducted to monitor alteration in rSO2 level profile for occlusion using near infrared spectroscopy (NIRS) methodology. NIRS signal data has been collected from twenty-three subjects with no reported musculoskeletal, psychiatric, or any other neurological deficits using Mespere NeurOs® oxymeter. Experimental data reveal that the oxygenation level decreases considerably due to arterial occlusion as compared to the healthy condition at 95% of confidence interval (p <.05). Dynamic time warping algorithm reveals that variation in rSO2 due to proposed thrombus geometry has more similarity with experimental study. [ABSTRACT FROM AUTHOR]

Big Data technology is one of the most promising organizational processes within the Healthcare and Pharmaceutical industry and crucial for any company that wants to preserve the competitive advantage in the market, where most of the organizational structures are already struggling with the right skills and knowledge to fully support existing business needs for storing and processing and even analyzing information. This paper aims to examine the extent to which new Big Data technology and data-related processes are developing different professionals skills and competencies within the Healthcare and Pharmaceutical industries, and creating sustainable development in addressing critical organizational challenges in recruiting, retaining, and discover professional skills that can fully support the advances and exponential growth of Big Data technology benefits. This research paper also highlights the significant aspects of Big Data in professional technical and process oriented skills development, and the influence it has on organizational business processes including how various internal functions will need to adapt to new circumstances with renewed competency and skills development programs for departments that are strongly connected to the business and analytical needs. We conducted a focus group with twenty-five industry based professionals' ranges from analysts to executive directors to better assess the necessary knowledge to answer the proposed research questions: (1) which professional skills can big data influence in employee development and (2) how can organizations adapt their employee skills to big data. Regarding the key research limitations/implications most of the article and research was built on the foundation of the literature review and the performed focus group. The conceptual recommendations and observations presented provide solid empirical evidence but should be subjected to more comprehensive, large-scale empirical testing and validation. It's recommended for future research a more extensive sample of companies, organizations, and interviewees. Studying a broader set of similar research questions in more homogeneous organizations could provide deeper insights into the process, governance, and stakeholder dimensions of Big Data within specific contexts. Therefore this study contributes to explore in-depth and systematically to what extent Big Data technology and processes are currently influencing the healthcare and pharmaceuticals industries where to the best of the authors' knowledge, it is the first focus group dealing with the presented research questions. [ABSTRACT FROM AUTHOR]

Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don't exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning. [ABSTRACT FROM AUTHOR]

The increasing amount of data produced by various biomedical and healthcare systems has led to a need for methodologies related to knowledge data discovery. Data mining (DM) offers a set of powerful techniques that allow the identification and extraction of relevant information from medical datasets, thus enabling doctors and patients to greatly benefit from DM, particularly in the case of diseases with high mortality and morbidity rates, such as heart disease (HD). Nonetheless, the use of raw medical data implies several challenges, such as missing data, noise, redundancy and high dimensionality, which make the extraction of useful and relevant information difficult and challenging. Intensive research has, therefore, recently begun in order to prepare raw healthcare data before knowledge extraction. In any knowledge data discovery (KDD) process, data preparation is the step prior to DM that deals with data imperfectness in order to improve its quality so as to satisfy the requirements and improve the performances of DM techniques. The objective of this paper is to perform a systematic mapping study (SMS) on data preparation for KDD in cardiology so as to provide an overview of the quantity and type of research carried out in this respect. The SMS consisted of a set of 58 selected papers published in the period January 2000 and December 2017. The selected studies were analyzed according to six criteria: year and channel of publication, preparation task, medical task, DM objective, research type and empirical type. The results show that a high amount of data preparation research was carried out in order to improve the performance of DM-based decision support systems in cardiology. Researchers were mainly interested in the data reduction preparation task and particularly in feature selection. Moreover, the majority of the selected studies focused on classification for the diagnosis of HD. Two main research types were identified in the selected studies: solution proposal and evaluation research, and the most frequently used empirical type was that of historical-based evaluation. [ABSTRACT FROM AUTHOR]

Images are powerful tools with which to convey human emotions, with different images stimulating diverse emotions. Numerous factors affect the emotions stimulated by the image, and many researchers have previously focused on low-level features such as color, texture and so on. Inspired by the successful use of deep convolutional neural networks (CNN) in the visual recognition field, we used a data augmentation method for small data sets to gain the sufficient number of the training dataset. In this paper, we use low-level features (color and texture features) of the image to assist the extraction of advanced features (image object category features and deep emotion features of images), which are automatically learned by deep networks, to obtain more effective image sentiment features. Then, we use the stack sparse auto-encoding network to recognize the emotions evoked by the image. Finally, high-level semantic descriptive phrases including image emotions and objects are output. Our experiments are carried out on the IAPS and GAPED data sets of the dimension space and the artphoto data set of the discrete space. Compared with the traditional manual extraction methods and other existing models, our method is superior to in terms of test performance. [ABSTRACT FROM AUTHOR]

In order to realize the automatic epileptic seizure detection, feature extraction and classification of electroencephalogram (EEG) signals are performed on the interictal, the pre-ictal, and the ictal status of epilepsy patients. There is no effective strategy for selecting the number of channels and spatial filters in feature extraction of multichannel EEG data. Therefore, this paper combined sparse idea and greedy search algorithm to improve the feature extraction of common space pattern. The feature extraction can effectively overcome the repeating selection problem of feature pattern in the eigenvector space by the traditional method. Then we used the Fisher linear discriminant analysis to realize the classification. The results show that the proposed method can get high classification accuracy using fewer data. For 10 subjects, the averaged accuracy of epilepsy detection is more than 99%. So, the detection of an epileptic seizure based on sparse features using Fisher linear discriminant analysis classifiers is more suitable for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life. [ABSTRACT FROM AUTHOR]

Unlike the traditional medical system, telecare medicine information system (TMIS) ensures that patients can get health-care services via the Internet at home. Authenticated key agreement protocol is very important for protecting the security in TMIS. Recently scholars have proposed a lot of authenticated key agreement protocols. In 2016, Chiou et al. demonstrated that Chen et al.'s authentication scheme fails to provide user's anonymity and message authentication and then proposed an enhanced scheme (Chiou et al., J. Med. Syst. 40(4):1-15, 2006) to overcome these drawbacks. In this paper, we demonstrate that Chiou et al.'s scheme is defenseless against key compromise impersonation (KCI) attack and also fails to provide forward security. Moreover, we propose a novel authentication scheme namely ICASME to overcome the mentioned weaknesses in this paper. Security analyses show that ICASME achieves the forward security and KCI attack resistance. In addition, it is proved that the time taken to implement the ICASME is not intolerable compared to the original protocol. [ABSTRACT FROM AUTHOR]

Mobile health (mHealth) intervention systems can employ adaptive strategies to interact with users. Instead of designing such complex strategies manually, reinforcement learning (RL) can be used to adaptively optimize intervention strategies concerning the user's context. In this paper, we focus on the issue of overwhelming interactions when learning a good adaptive strategy for the user in RL-based mHealth intervention agents. We present a data-driven approach integrating psychological insights and knowledge of historical data. It allows RL agents to optimize the strategy of delivering context-aware notifications from empirical data when counterfactual information (user responses when receiving notifications) is missing. Our approach also considers a constraint on the frequency of notifications, which reduces the interaction burden for users. We evaluated our approach in several simulation scenarios using real large-scale running data. The results indicate that our RL agent can deliver notifications in a manner that realizes a higher behavioral impact than context-blind strategies. [ABSTRACT FROM AUTHOR]

In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead to a rapid diminution of their physical abilities, cognitive impairment, and reduced quality of life. This paper proposes a personalized support system for cancer survivors based on a cohort and trajectory analysis (CTA) module integrated within an agent-based personalized chatbot named EREBOTS. The CTA module relies on survival estimation models, machine learning, and deep learning techniques. It provides clinicians with supporting evidence for choosing a personalized treatment, while allowing patients to benefit from tailored suggestions adapted to their conditions and trajectories. The development of the CTA within the EREBOTS framework enables to effectively evaluate the significance of prognostic variables, detect patient's high-risk markers, and support treatment decisions. [ABSTRACT FROM AUTHOR]

The main novelty of this paper is presenting the adaptation of Gesture Description Language (GDL) methodology to sport and rehabilitation data analysis and classification. In this paper we showed that Lua language can be successfully used for adaptation of the GDL classifier to those tasks. The newly applied scripting language allows easily extension and integration of classifier with other software technologies and applications. The obtained execution speed allows using the methodology in the real-time motion capture data processing where capturing frequency differs from 100 Hz to even 500 Hz depending on number of features or classes to be calculated and recognized. Due to this fact the proposed methodology can be used to the high-end motion capture system. We anticipate that using novel, efficient and effective method will highly help both sport trainers and physiotherapist in they practice. The proposed approach can be directly applied to motion capture data kinematics analysis (evaluation of motion without regard to the forces that cause that motion). The ability to apply pattern recognition methods for GDL description can be utilized in virtual reality environment and used for sport training or rehabilitation treatment. [ABSTRACT FROM AUTHOR]

This paper presents an empirical study of a formative neural network-based assessment approach by using mobile technology to provide pharmacy students with intelligent diagnostic feedback. An unsupervised learning algorithm was integrated with an audience response system called SIDRA in order to generate states that collect some commonality in responses to questions and add diagnostic feedback for guided learning. A total of 89 pharmacy students enrolled on a Human Anatomy course were taught using two different teaching methods. Forty-four students employed intelligent SIDRA (i-SIDRA), whereas 45 students received the same training but without using i-SIDRA. A statistically significant difference was found between the experimental group (i-SIDRA) and the control group (traditional learning methodology), with T (87) = 6.598, p < 0.001. In four MCQs tests, the difference between the number of correct answers in the first attempt and in the last attempt was also studied. A global effect size of 0.644 was achieved in the meta-analysis carried out. The students expressed satisfaction with the content provided by i-SIDRA and the methodology used during the process of learning anatomy (M = 4.59). The new empirical contribution presented in this paper allows instructors to perform post hoc analyses of each particular student's progress to ensure appropriate training. [ABSTRACT FROM AUTHOR]

In 2010, the Addendum D to ASHRAE Standard 170, 'Ventilation of healthcare facilities,' lowered the minimum relative humidity (RH) requirement of anesthetizing locations (including operating rooms, operating/surgical cystoscopic rooms, delivery rooms (Caesarean), recovery rooms, critical and intensive care, newborn intensive care, treatment rooms, trauma rooms (crisis or shock), laser eye rooms, newborn nursery suites, and endoscopy rooms) from 30 % to 20 %. The new minimum limit was adopted based on the results of a review paper that suggested that lowering humidity levels will have little or no impact on providing a safe environment for patients, staff, or medical equipment. That review paper reached this conclusion by assuming that there were no medical device failures due to electrostatic discharge (ESD). However, in an examination of the FDA's MAUDE database of reported defects and recalls, we identified numerous medical device failures explicitly due to ESD. This paper presents technical reliability and safety concerns regarding the new guidelines and recommends that such changes should not be implemented and that the guidelines should be revoked. [ABSTRACT FROM AUTHOR]

The overall demographic profile of current societies point to a significant growth of the elderly people. Associated with the increase of the average hope of life and consequent increase in chronic diseases, there is the need for protection and daily care. Increasing investments in technology, such as Ambient Assisted Living (AAL) solutions, promote the quality of live extending the time people can live in their desired environment. This paper proposes the design, deployment, and real testbed of an e-health wearable monitoring system based on the integration of several AAL tools and platforms for elderly's bio-signals monitoring. This solution includes electrocardiography (ECG), respiration rate, beats per minute, body temperature, and falls detention and notification. The paper also describes, in detail, the real pilot and analyzes some early results concerning the users quality of experience, and the found results are very promising. [ABSTRACT FROM AUTHOR]

Preoperative path planning plays a critical role in vascular access surgery. Vascular access surgery has superior difficulties and requires long training periods as well as precise operation. Yet doctors are on different leves, thus bulky size of blood vessels is usually chosen to undergo surgery and other possible optimal path is not considered. Moreover, patients and surgeons will suffer from X-ray radiation during the surgical procedure. The study proposed an improved ant colony algorithm to plan a vascular optimal three-dimensional path with overall consideration of factors such as catheter diameter, vascular length, diameter as well as the curvature and torsion. To protect the doctor and patient from exposing to X-ray long-term, the paper adopted augmented reality technology to register the reconstructed vascular model and physical model meanwhile, locate catheter by the electromagnetic tracking system and used Head Mounted Display to show the planning path in real time and monitor catheter push procedure. The experiment manifests reasonableness of preoperative path planning and proves the reliability of the algorithm. The augmented reality experiment real time and accurately displays the vascular phantom model, planning path and the catheter trajectory and proves the feasibility of this method. The paper presented a useful and feasible surgical scheme which was based on the improved ant colony algorithm to plan vascular three-dimensional path in augmented reality. The study possessed practical guiding significance in preoperative path planning, intraoperative catheter guiding and surgical training, which provided a theoretical method of path planning for vascular access surgery. It was a safe and reliable path planning approach and possessed practical reference value. [ABSTRACT FROM AUTHOR]

In order to access remote medical server, generally the patients utilize smart card to login to the server. It has been observed that most of the user (patient) authentication protocols suffer from smart card stolen attack that means the attacker can mount several common attacks after extracting smart card information. Recently, Lu et al.'s proposes a session key agreement protocol between the patient and remote medical server and claims that the same protocol is secure against relevant security attacks. However, this paper presents several security attacks on Lu et al.'s protocol such as identity trace attack, new smart card issue attack, patient impersonation attack and medical server impersonation attack. In order to fix the mentioned security pitfalls including smart card stolen attack, this paper proposes an efficient remote mutual authentication protocol using smart card. We have then simulated the proposed protocol using widely-accepted AVISPA simulation tool whose results make certain that the same protocol is secure against active and passive attacks including replay and man-in-the-middle attacks. Moreover, the rigorous security analysis proves that the proposed protocol provides strong security protection on the relevant security attacks including smart card stolen attack. We compare the proposed scheme with several related schemes in terms of computation cost and communication cost as well as security functionalities. It has been observed that the proposed scheme is comparatively better than related existing schemes. [ABSTRACT FROM AUTHOR]

Over the last 20 years, China's infertility rate has risen from 3% to 12.5%–15%. Infertility has become the third largest disease following cancer and cardiovascular disease. Then, the in vitro fertilization and embryo transfer (IVF-ET) becomes more and more important in infertility treatment field. However, the reported success rate for IVT-ET is 30%–40% and costs are gradually rising. Meanwhile, to increase success rates and decrease costs, the optimal selection of the IVF-ET treatment strategy is crucial. In a clinical work, the IVF-ET treatment strategy selection is always based on the experience of the doctor without a uniform standard. To solve this important and complex problem, we proposed an artificial intelligence (AI)-based optimal treatment strategy selection system to extract implicit knowledge from clinical data for new and returning patients, by mimicking the IVF-ET process and analysing a myriad of treatment decisions. We demonstrated that the performance of the model was different in 10 AI classification algorithms. Hence, we need to select the optimal method for predicting patient pregnancy result in different IVF-ET treatment strategies. Moreover, feature ranking is determined in the proposed model to measure the importance of each patient characteristics. Therefore, better advice can be provided for individual patient characteristics, doctors can provide more valid suggestions regarding certain patient characteristics to improve the accuracy of diagnosis and efficiency. [ABSTRACT FROM AUTHOR]

The aim of this paper is to describe the design and the preliminary validation of a platform developed to collect and automatically analyze biomedical signals for risk assessment of vascular events and falls in hypertensive patients. This m-health platform, based on cloud computing, was designed to be flexible, extensible, and transparent, and to provide proactive remote monitoring via data-mining functionalities. A retrospective study was conducted to train and test the platform. The developed system was able to predict a future vascular event within the next 12 months with an accuracy rate of 84 % and to identify fallers with an accuracy rate of 72 %. In an ongoing prospective trial, almost all the recruited patients accepted favorably the system with a limited rate of inadherences causing data losses (<20 %). The developed platform supported clinical decision by processing tele-monitored data and providing quick and accurate risk assessment of vascular events and falls. [ABSTRACT FROM AUTHOR]

Medical technology makes an inevitable trend for the elderly population, therefore the intelligent home care is an important direction for science and technology development, in particular, elderly in-home safety management issues become more and more important. In this research, a low of operation algorithm and using the triangular pattern rule are proposed, then can quickly detect fall-down movements of humanoid by the installation of a robot with camera vision at home that will be able to judge the fall-down movements of in-home elderly people in real time. In this paper, it will present a preliminary design and experimental results of fall-down movements from body posture that utilizes image pre-processing and three triangular-mass-central points to extract the characteristics. The result shows that the proposed method would adopt some characteristic value and the accuracy can reach up to 90 % for a single character posture. Furthermore the accuracy can be up to 100 % when a continuous-time sampling criterion and support vector machine (SVM) classifier are used. [ABSTRACT FROM AUTHOR]

The objective of this evaluation study is to assess a method for standard based medical data visualization. The method allows flexible and customizable visualization for ISO 13606 archetype based medical data. The chosen evaluation concept is based the Guideline for Good Evaluation Practice in Health Informatics (GEP-HI). The stages of the study were identified. Each stage got a detailed description. We also identified the participants and their required qualifications and responsibilities. The evaluation location was described in details. The evaluation metrics were defined. The questionnaires for doctors, patients and experts were developed to fulfill the requirements of the evaluation study. The study was performed in Tomsk, Russia. 30 patients and 5 doctors participated in the study. The overall performance of the users reached the expert level by the end of the study. Patients as well as medical staff stated in their comments that the usability of the system was high, and they preferred it to the previously used paper-based and computer based systems. This was also shown by the high level of satisfaction measured within our study. The visualization approach, integrated into the electronic health record, was well accepted in our pilot setting with high usability scores from patients and doctors alike. The results showed the efficiency for both modeling and visualization part of the system [ABSTRACT FROM AUTHOR]

Secondary use of medical big data is increasingly popular in healthcare services and clinical research. Understanding the logic behind medical big data demonstrates tendencies in hospital information technology and shows great significance for hospital information systems that are designing and expanding services. Big data has four characteristics - Volume, Variety, Velocity and Value (the 4 Vs) - that make traditional systems incapable of processing these data using standalones. Apache Hadoop MapReduce is a promising software framework for developing applications that process vast amounts of data in parallel with large clusters of commodity hardware in a reliable, fault-tolerant manner. With the Hadoop framework and MapReduce application program interface (API), we can more easily develop our own MapReduce applications to run on a Hadoop framework that can scale up from a single node to thousands of machines. This paper investigates a practical case of a Hadoop-based medical big data processing system. We developed this system to intelligently process medical big data and uncover some features of hospital information system user behaviors. This paper studies user behaviors regarding various data produced by different hospital information systems for daily work. In this paper, we also built a five-node Hadoop cluster to execute distributed MapReduce algorithms. Our distributed algorithms show promise in facilitating efficient data processing with medical big data in healthcare services and clinical research compared with single nodes. Additionally, with medical big data analytics, we can design our hospital information systems to be much more intelligent and easier to use by making personalized recommendations. [ABSTRACT FROM AUTHOR]

The article discuses a review paper which aimed to identify the most threatening types of distributed denial of service (DDoS) attacks that affect the availability of a cloud-assisted Wireless Body Area Networks (WBANS) and review the state-of-the-art detection mechanisms for the identified DDoS attacks. The research involved a systematic review of the existing literature about security issues in a cloud-assisted WBAN.

In recent years, artificial intelligence-based computer aided diagnosis (CAD) system for the hepatitis has made great progress. Especially, the complex models such as deep learning achieve better performance than the simple ones due to the nonlinear hypotheses of the real world clinical data. However,complex model as a black box, which ignores why it make a certain decision, causes the model distrust from clinicians. To solve these issues, an explainable artificial intelligence (XAI) framework is proposed in this paper to give the global and local interpretation of auxiliary diagnosis of hepatitis while retaining the good prediction performance. First, a public hepatitis classification benchmark from UCI is used to test the feasibility of the framework. Then, the transparent and black-box machine learning models are both employed to forecast the hepatitis deterioration. The transparent models such as logistic regression (LR), decision tree (DT)and k-nearest neighbor (KNN) are picked. While the black-box model such as the eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), random forests (RF) are selected. Finally, the SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME) and Partial Dependence Plots (PDP) are utilized to improve the model interpretation of liver disease. The experimental results show that the complex models outperform the simple ones. The developed RF achieves the highest accuracy (91.9%) among all the models. The proposed framework combining the global and local interpretable methods improves the transparency of complex models, and gets insight into the judgments from the complex models, thereby guiding the treatment strategy and improving the prognosis of hepatitis patients. In addition, the proposed framework could also assist the clinical data scientists to design a more appropriate structure of CAD. [ABSTRACT FROM AUTHOR]

In the context of the VASelfCare project, we developed an application prototype of an intelligent anthropomorphic virtual assistant. Designed as a relational agent, the virtual assistant has the role of supporting older people with Type 2 Diabetes Mellitus (T2D) in medication adherence and lifestyle changes. Our paper has two goals: describing the essentials of this prototype, and reporting on usability evaluation. We describe the general architecture of the prototype, including the graphical component, and focus on its main feature: the incorporation, in the way the dialogue flows, of Behavior Change Techniques, identified through a theoretical framework, the Behaviour Change Wheel. Usability was experimentally evaluated in field tests in a purposive sample of 20 participants (11 older adults with T2D and 9 experts). The Portuguese version of the System Usability Scale was employed, supplemented with qualitative data from open questions, diaries, digital notes and telephone follow-ups. The aggregated mean SUS score was 73,75 (SD 13,31), which corresponds to a borderline rating of excellent. Textual data were content analyzed and will be prioritized to further improve usability. [ABSTRACT FROM AUTHOR]

Employing software engineering to build an integrated, standardized, and scalable solution is closely associated with the healthcare domain. Furthermore, new diagnostic techniques have been developed to obtain better results in less time, saving costs, and bringing services closer to the most unprotected areas. This paper presents the integration of a top-notch component, such as hardware, software, telecommunications, and medical equipment, to produce a complete system of Electronic Health Record (EHR). The EHR implementation aims to contribute to the expansion of the health services offer concerning people who live in locations where typically have difficult access to medical care. The methodology throughout the work is a Strategic Planning to set priorities, focus energy and resources, strengthen operations, ensure that directors, managers, employees, and other stakeholders are working toward common goals, establish agreement around intended outcomes/results. A medical and technical team is incorporated to complete the tasks of process and requirements analysis, software coding and design, technical support, training, and coaching for EHR system users throughout the implementation process. The adoption of those tools reflect notably some expected results and benefits on patient care. The EHR implementation ensures that information collection does not duplicate already existing information or duplicate effort and maximize the practical use of the data collected. Moreover, the EHR reduces mistakes in hospital readmissions, improves paperwork, promotes the progress of the state's health care system providing emergency, specialty, and primary health care in a rural area of Campeche. The EHR implementation is critical to support decision making and to promote public health. The total number of consults increased markedly from 2012 (14021) to 2019 (34751). The most commonly treated diseases in this region of Mexico are hypertension (17632) and diabetes (13156). The best results are obtained in the Nutrition (20,61%) and clinical psychology services (16,67%), and the worst levels are registered in pediatric and surgical oncology services where only 1,59% and 1,97% of the patients are admitted in less than 30 min, respectively. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes. [ABSTRACT FROM AUTHOR]

Virtual Reality (VR) is being integrated into many different areas of our lives, from industrial engineering to video-games, and also including teaching and education. We have several examples where VR has been used to engage students and facilitate their 3D spatial understanding, but can VR help also teachers? What are the benefits teachers can obtain on using VR applications? In this paper we present an application (VR4Health) designed to allow students to directly inspect 3D models of several human organs by using Virtual Reality systems. The application is designed to be used in an HMD device autonomously as a self-learning tool and also reports information to teachers in order that he/she becomes aware of what the students do and can redirect his/her work to the concrete necessities of the student. We evaluate both the students' and the teachers' perception by doing an experiment and asking them to fill-in a questionnaire at the end of the experiment. [ABSTRACT FROM AUTHOR]

The distribution of fiducial markers is one of the main factors affected the accuracy of optical navigation system. However, many studies have been focused on improving the fiducial registration accuracy or the target registration accuracy, but few solutions involve optimization model for the distribution of fiducial markers. In this paper, we propose an optimization model for the distribution of fiducial markers to improve the optical navigation accuracy. The strategy of optimization model is reducing the distribution from three dimensional to two dimensional to obtain the 2D optimal distribution by using optimization algorithm in terms of the marker number and the expectation equation of target registration error (TRE), and then extend the 2D optimal distribution in two dimensional to three dimensional to calculate the optimal distribution according to the distance parameter and the expectation equation of TRE. The results of the experiments show that the averaged TRE for the human phantom is approximately 1.00 mm by applying the proposed optimization model, and the averaged TRE for the abdominal phantom is 0.59 mm. The experimental results of liver simulator model and ex-vivo porcine liver model show that the proposed optimization model can be effectively applied in liver intervention. [ABSTRACT FROM AUTHOR]

One of the most common chronic diseases that causes missed school days and workdays is pediatric asthma, producing education, health and economic delays in low and middle-income countries. A patient-centered remarkable therapeutic strategy in the prevention and treatment of chronic diseases is the Therapeutic Patient Education (TPE) which denotes an outstanding difference in the outcomes, from the awareness, to the personal treatment adaption to prevent crises. In this paper, an intervention of TPE with learning objects (LOs) of a pediatric asthma course, designed to train the uncontrolled patient and the caregiver as a team on the specific knowledge to self-control the disease, was carried out at a health facility in Mexico. The Asthma Control Test (ACT) was used and the learning performance was evaluated with formative quizzes. A randomized controlled trial was designed, with pairs of children patients and caregivers during 60 days; the control group was trained with the usual approach, receiving general information about the disease. ACT measures showed statistical significance for the TPE group after the use of the LOs, achieving a controlled state, while the control group did not show difference. Also, the intervention group obtained a higher score in learning performance assessment. The users learned how to avoid risks, the personal triggers of the patient, how to correctly use the different treatments and accessories, consequently, they learned how to self-manage the disease. Our study also confirmed that control of asthma is not only a matter of knowledge, but financial issues to afford the treatment. [ABSTRACT FROM AUTHOR]

Lung cancer is considered one of the deadliest diseases in the world. An early and accurate diagnosis aims to promote the detection and characterization of pulmonary nodules, which is of vital importance to increase the patients' survival rates. The mentioned characterization is done through a segmentation process, facing several challenges due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper tackles pulmonary nodule segmentation in computed tomography scans proposing three distinct methodologies. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the filter's support points, matching the border coordinates. The remaining approaches are Deep Learning based, using the U-Net and a novel network called SegU-Net to achieve the same goal. Their performance is compared, as this work aims to identify the most promising tool to improve nodule characterization. All methodologies used 2653 nodules from the LIDC database, achieving a Dice score of 0.663, 0.830, and 0.823 for the SBF, U-Net and SegU-Net respectively. This way, the U-Net based models yield more identical results to the ground truth reference annotated by specialists, thus being a more reliable approach for the proposed exercise. The novel network revealed similar scores to the U-Net, while at the same time reducing computational cost and improving memory efficiency. Consequently, such study may contribute to the possible implementation of this model in a decision support system, assisting the physicians in establishing a reliable diagnosis of lung pathologies based on this segmentation task. [ABSTRACT FROM AUTHOR]

Approximately 23% of patients discharged from primary healthcare facilities are readmitted within 30 days at an annual cost of roughly $42 billion. To remedy this problem, healthcare providers are attempting to deploy readmission risk estimation tools, but how they might be used in the traditional, human-expert-centered decision process is not well understood. One such tool estimates readmission risk based on 50 patient-specific factors. This paper reports on a study performed in collaboration with Order of St. Francis Healthcare to determine how healthcare workers' own risk estimates are influenced by the tool, specifically testing the hypothesis that they will first anchor towards tool results while making adjustments based on their expertise, and then make further adjustments when additional human expert opinions are presented. Task analysis was performed, fictional patient scenarios were developed, and a survey of 56 subjects in two stratified groups of case managers was conducted. Data from the control and experiment groups were analyzed using ANOVA/GLM and t-tests. Results indicate that the healthcare workers' risk estimates were influenced by the anchor provided by the tool, then adjusted based on their expertise. The workers further adjusted their estimates in response to new expert human inputs. Thus, a reliance on both the predictive model and human expertise was observed. [ABSTRACT FROM AUTHOR]

Health information technology capabilities in some healthcare sectors, such as nursing homes, are not well understood because measures for information technology uptake have not been fully developed, tested, validated, or measured consistently. The paper provides a report of the development and testing of a new instrument measuring nursing home information technology maturity and stage of maturity. Methods incorporated a four round Delphi panel composed of 31 nursing home experts from across the nation who reported the highest levels of information technology sophistication in a separate national survey. Experts recommended 183 content items for 27 different content areas specifying the measure of information technology maturity. Additionally, experts ranked each of the 183 content items using an IT maturity instrument containing seven stages (stages 0–6) of information technology maturity. The majority of content items (40% (n = 74)) were associated with information technology maturity stage 4, corresponding to facilities with external connectivity capability. Over 11% of the content items were at the highest maturity stage (Stage 5 and 6). Content areas with content items at the highest stage of maturity are reflected in nursing homes that have technology available for residents or their representatives and used extensively in resident care. An instrument to assess nursing home IT maturity and stage of maturity has important implications for understanding health service delivery systems, regulatory efforts, patient safety and quality of care. [ABSTRACT FROM AUTHOR]

There has been an increasing attention to the study of stress. Particularly, college students often experience high levels of stress that are linked to several negative outcomes concerning academic functioning, physical, and mental health. In this paper, we introduce the EuStress Solution, that aims to create an Information System to monitor and assess, continuously and in real-time, the stress levels of the students in order to predict burnout. The Information System will use a measuring instrument based on wearable device and machine learning techniques to collect and process stress-related data from the students without their explicit interaction. In the present study, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. We performed different statistical tests in order to develop a complex and intelligent model. Results showed the neural network had the better model fit. [ABSTRACT FROM AUTHOR]

Heterogeneity of people with diabetes makes maintaining blood glucose control and achieving therapy adherence a challenge. It is fundamental that patients get actively involved in the management of the disease in their living environments. The objective of this paper is to evaluate the use and acceptance of a self-management system for diabetes developed with User Centered Design Principles in community settings. Persons with diabetes and health professionals were involved the design, development and evaluation of the self-management system; which comprised three iterative cycles: scenario definition, user archetype definition and system development. A comprehensive system was developed integrating modules for the management of blood glucose levels, medication, food intake habits, physical activity, diabetes education and messaging. The system was adapted for two types of principal users (personas): Type 1 Diabetes user and Type 2 Diabetes user. The system was evaluated by assessing the use, the compliance, the attractiveness and perceived usefulness in a multicenter randomized pilot study involving 20 patients and 24 treating professionals for a period of four weeks. Usage and compliance of the co-designed system was compared during the first and the last two weeks of the study, showing a significantly improved behaviour of patients towards the system for each of the modules. This resulted in a successful adoption by both type of personas. Only the medication module showed a significantly different use and compliance (p= 0.01) which can be explained by the different therapeutic course of the two types of diabetes. The involvement of patients to make their own decisions and choices form design stages was key for the adoption of a self-management system for diabetes. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop an autonomous tracking algorithm based on adaptive fusion kinematics method, the autonomous laparoscope control algorithm and adaptive fusion kinematics method are proposed for semi-autonomous surgery, focus on solving the problems of autonomous laparoscope field of view control for surgical robot system. A novel autonomous tracking algorithm is proposed. To realize more robust tracking, an adaptive fusion kinematics method based on fuzzy logic is proposed, the method adaptive associates the kinematics information of surgical robot system and the laparoscope information. The proposed methods are implemented on the laparoscopic minimally invasive surgical robot system which is developed by our laboratory. Two experiments are carried out, the results indicate that the accurate autonomous field of view control is achieved with the addition of laparoscope information, laparoscopic motion frequency is reduced, the methods can avoid the laparoscope continuous motion and ensure the stability of field of view. The proposed methods improve the intelligence level of surgical robot system. [ABSTRACT FROM AUTHOR]

This paper presents a high precision and low computational complexity premature ventricular contraction (PVC) assessment method for the ECG human-machine interface device. The original signals are preprocessed by integrated filters. Then, R points and surrounding feature points are determined by corresponding detection algorithms. On this basis, a complex feature set and feature matrices are obtained according to the position feature points. Finally, an exponential Minkowski distance method is proposed for PVC recognition. Both public dataset and clinical experiments were utilized to verify the effectiveness and superiority of the proposed method. The results show that our R peak detection algorithm can substantially reduce the error rate, and obtained 98.97% accuracy for QRS complexes. Meanwhile, the accuracy of PVC recognition was 98.69% for the MIT-BIH database and 98.49% for clinical tests. Moreover, benefiting from the lightweight of our model, it can be easily applied to portable healthcare devices for human-computer interaction. [ABSTRACT FROM AUTHOR]

The TNM staging system is universally used for classification of cancer. This system is limited since it uses only three factors (tumor size, extent of spread to lymph nodes, and status of distant metastasis) to generate stage groups. To provide a more accurate description of cancer and thus better patient care, additional factors or variables should be used to classify cancer. In this paper we propose a hierarchical clustering algorithm to develop prognostic systems that classify cancer according to multiple prognostic factors. This algorithm has many potential applications in augmenting the data currently obtained in a staging system by allowing more prognostic factors to be incorporated. The algorithm clusters combinations of prognostic factors that are formed using categories of factors. The dissimilarity between two combinations is determined by the area between two corresponding survival curves. Groups from cutting the dendrogram and survival curves of the individual groups define our prognostic systems that classify patients using survival outcomes. A demonstration of the proposed algorithm is given for patients with breast cancer from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. [ABSTRACT FROM AUTHOR]

A good primary health care is the base for a better healthcare system. Taking a good decision on time by the primary health care physician could have a huge repercussion. In order to ease the diagnosis task arise the Decision Support Systems (DSS), which offer counselling instead of refresh the medical knowledge, in a profession where it is still learning every day. The implementation of these systems in diseases which are a frequent cause of visit to the doctor like ophthalmologic pathologies are, which affect directly to our quality of life, takes more importance. This paper aims to develop OphthalDSS, a totally new mobile DSS for red eye diseases diagnosis. The main utilities that OphthalDSS offers will be a study guide for medical students and a clinical decision support system for primary care professionals. Other important goal of this paper is to show the user experience results after OphthalDSS being used by medical students of the University of Valladolid. For achieving the main purpose of this research work, a decision algorithm will be developed and implemented by an Android mobile application. Moreover, the Quality of Experience (QoE) has been evaluated by the students through the questions of a short inquiry. The app developed which implements the algorithm OphthalDSS is capable of diagnose more than 30 eye's anterior segment diseases. A total of 67 medical students have evaluated the QoE. The students find the diseases' information presented very valuable, the appearance is adequate, it is always available and they have ever found what they were looking for. Furthermore, the students think that their quality of life has not been improved using the app and they can do the same without using the OphthalDSS app. OphthalDSS is easy to use, which is capable of diagnose more than 30 ocular diseases in addition to be used as a DSS tool as an educational tool at the same time. [ABSTRACT FROM AUTHOR]