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To evaluate the usefulness of applying computed tomography (CT) images reconstructed by a deep learning super-resolution method to the clinical scenario of planning a real bronchoscopy procedure. We trained a super-resolution generative adversarial network (SRGAN) to reconstruct CT images to high-resolution (SRGANrc). We tasked three pulmonologists with evaluating the quality of the CT images and the derived virtual bronchoscopies. We also compared the number of bronchi that were segmented by an automatic commercial program with the number of bronchi segmented in different processed thin-sectioned CT images. Regarding the human visual score, the original thin-sectioned CT images received more votes than the reconstructed CT images (SRGANrc) (29 votes versus eight votes). As for the human classification of four high-resolution CT images, the majority of images (83.7%) were classified correctly. Four out of 23 virtual bronchoscopies derived from super-resolution CT images were considered superior. The number of automatically segmented bronchi in super-resolution CT images was on average 1.5 less than that in the original thin-sliced CT images (mean bronchi: 15.1 vs. 16.6). The reconstruction of super-resolution CT images through the SRGAN may have limited applications in the clinical scenarios of our study. In addition to improving the deep-learning algorithm, we need more clinical implementation tests to discover its value.

SummaryBackground: As a result of the disease‘s high prevalence, chronic kidney disease (CKD) has become a global public health problem. A clinical decision support system that integrates with computer-interpretable guidelines (CIGs) should improve clinical outcomes and help to ensure patient safety.Objectives: The openEHR guideline definition language (GDL) is a formal language used to represent CIGs. This study explores the feasibility of using a GDL approach for CKD; it also attempts to identify any potential gaps between the ideal concept and reality.Methods: Using the Kidney Disease Improving Global Outcomes (KDIGO) anemia guideline as material, we designed a development workflow in order to establish a series of GDL guidelines. Focus group discussions were conducted in order to identify important issues related to GDL implementation.Results: Ten GDL guidelines and 37 archetypes were established using the KDIGO guideline document. For the focus group discussions, 16 clinicians and 22 IT experts were recruited and their perceptions, opinions and attitudes towards the GDL approach were explored. Both groups provided positive feedback regarding the GDL approach, but raised various concerns about GDL implementation.Conclusions: Based on the findings of this study, we identified some potential gaps that might exist during implementation between the GDL concept and reality. Three directions remain to be investigated in the future. Two of them are related to the openEHR GDL approach. Firstly, there is a need for the editing tool to be made more sophisticated. Secondly, there needs to be integration of the present approach into non openEHR-based hospital information systems. The last direction focuses on the applicability of guidelines and involves developing a method to resolve any conflicts that occur with insurance payment regulations.

Eligibility criteria among hundreds of National Health Insurance Research Database (NHIRD) research papers have similar constituent elements, such as demographic characteristics or diagnostic codes. The study results of the same disease could vary among different research due to the variation of the criteria statements, therefore the narrative patterns analysis tool would be helpful for summarizing the knowledge implicitly contained in the eligibility criteria. In this study, we developed a series of R-based text processing methods to extract the narrative eligibility criteria in NHIRD papers by simplifying the article titles and content paragraphs, identifying medical concepts and abbreviations, then detecting basic demographic characteristics and ICD-9-CM diagnosis codes. Although there is still room for improvement on study type identifying, the high performance in classifying the study type, detecting age restrictions and extracting ICD-9-CM codes still shows the system usefulness for the analysis of eligibility criteria.

Background: The National Health Insurance program (NHI) in Taiwan has provided hospice services since 2000, and it was expanded to noncancer illnesses in September 2009. The issues of noncancer hospice care and the impact of the expanded hospice policy remain unclear.Data were collected retrospectively from claims data of hospice admissions using the NHI Research Database of 2005-2010.A total of 359 noncancer subjects and 412 hospice admissions were enrolled; 1795 age- and gender-matched cancer patients and 2578 hospice admissions were selected as a comparison group. Noncancer hospice care increased markedly after the third quartile of 2009. The most common noncancer diagnosis was "other diseases of the lung" (23.9%). The noncancer subjects had a significantly lower frequency of admissions, lower Charlson Comorbidity Index (CCI) scores, shorter hospice stay, and higher mortality rate than the cancer subjects. Family physicians provided the majority of hospice services in both groups. Acute low respiratory conditions (ALRC) were the most common acute comorbidity in deceased subjects. The noncancer decedents had more ALRC, sepsis/bacteremia, nontraumatic shock, acute myocardial infarctions, and esophageal varicose bleeding than the comparison group. The mean inpatient charges differed insignificantly between both groups. The noncancer subjects correlated negatively with CCI (odds ratio [OR] 0.59 in all hospice admissions; 0.63 in decedents), but positively with a hospice stay ≤3 days, mortality, sepsis/bacteremia, ALRC, nontraumatic shock, and acute myocardial infarctions compared with the cancer subjects (OR 1.42, 1.98, 2.24, 2.36, 2.17, and 11.68, respectively, adjusted by CCI).The expanded palliative care policy has impacted positively on noncancer hospice care in Taiwan. The terminal noncancer patients had higher risks for short hospice stay, sepsis, nontraumatic shock, and respiratory and heart problems than the cancer subjects. Early referral to hospices is required for terminal patients in Taiwan. The CCI had a limited role for cost/severity evaluations of hospice care.

This case study reports on the utilization of telemedicine to support the management of the burns treatment in the islands of Sao Tome and Principe by Taipei Medical University- affiliated hospital in Taiwan. The authors share experiences about usage of telemedicine to support treatment of the burn victims in a low-income country that receive reconstructive surgery in a developed country. Throughout the entire care process, telemedicine has been used not only to provide an expert advice from distance but also to help establish and main- tain the doctor-patient relationship, to keep patients in contact with their families, and to help educate and consult the medical personal physically present in Sao Tome and Principe. This case study presents the details of how this process has been conducted to date, on what were learned from this process, and on issues that should be considered to improve this process in the future. The authors plan to create instructional videos and post them on YouTube to aid clinical workers providing similar treatment during the acute care and rehabilitation process and also to support eLearning in many situations where it otherwise is not possible to use videoconferencing to establish real-time contact between doctors at the local site and remote specialists. (J Burn Care Res 2012;33:e207-e212)

Genetic studies in several human autoimmune diseases suggest that the pericentromeric region of chromosome 16 might harbor an autoimmune modifier gene. We hypothesized that the sodium-dependent glucose cotransporter gene SLC5A11 is such a gene, and so might interact with immune-related genes. Herein, this hypothesis was tested in a genetic evaluation of the multiple gene effect in systemic lupus erythematosus (SLE). We used the case–control candidate gene association approach. Eight immune-related genes involved in inflammation and autoantibody generation and clear-up [interleukin 1 receptor antagonist (IL1RN), interleukin 1-beta (IL1-b), tumor necrosis factor-alpha (TNF-a), lymphotoxinalpha (LTA), tumor necrosis factor ligand superfamily, member 6 (TNFSF6), programmed cell death 1 (PDCD1), C2, and complement component 4 (C4)] were selected for study. Frequency of each candidate’s genotype and allele between case and control were compared. Results were stratified by reanalyzing genotype data with relevant symptoms. Finally, improved computational data mining was used to analyze the phenotypes in a large data set. In the frequency analysis, only IL1-b was significantly associated with SLE. Stratification analysis showed a significant association with SLE symptoms between SLC5A11 and the other immune-related genes, with the exceptions of TNFSF6 and C4. SLC5A11 was significantly associated with low C4 (as was TNF-a), anti-Smith antibody (anti-Sm) (as was C2), serositis, and alopecia. Finally, SLC5A11 interacted with PDCD1, TNF-a, LTA, and C4. After our study, we concluded that SLC5A11 is involved with some immune effects and interacts with immune-related gene(s), consistent with its function as an autoimmune modifier gene. Furthermore, SLC5A11 might induce apoptosis through the TNF-a, PDCD1 pathway. The present genotype–phenotype mapping approach should be applicable to genetic study of other complex diseases.

We assessed the feasibility of tele-ophthalmology in a remote location, Tungyin, an island 200 km from Taiwan, which has no ophthalmologist. Screening for eye diseases was carried out among residents aged 40 years or more. A total of 113 subjects, approximately 31% of the whole population, were enrolled in the screening programme. Images were transmitted (via ADSL) to a retinal specialist in Taiwan for diagnosis. The average processing time, excluding the time for copying files, was 6.4 s (SD 2.1) per subject. Transmission took 60–90 s for most of the images (83%). The average time required to make a diagnosis for each subject, including data entry, was approximately 34 s (SD 18). In screening for retinopathy, the detection rate with digital imaging (8.8%) was two times higher than with indirect ophthalmoscopy (4.4%). In 12% of cases macular degeneration was identified, and in 6% there were mild or moderate problems with the optic disc. Community-based screening for four categories of eye disease was successfully demonstrated using store-and-forward tele-ophthalmology.

Data mining, also known as Knowledge-Discovery in Databases (KDD), is the process of automatically searching large volumes of data for patterns. For instance, a clinical pattern might indicate a female who have diabetes or hypertension are easier suffered from stroke for 5 years in a future. Then, a physician can learn valuable knowledge from the data mining processes. Here, we present a study focused on the investigation of the application of artificial intelligence and data mining techniques to the prediction models of breast cancer. The artificial neural network, decision tree, logistic regression, and genetic algorithm were used for the comparative studies and the accuracy and positive predictive value of each algorithm were used as the evaluation indicators. 699 records acquired from the breast cancer patients at the University of Wisconsin, nine predictor variables, and one outcome variable were incorporated for the data analysis followed by the tenfold cross-validation. The results revealed that the accuracies of logistic regression model were 0.9434 (sensitivity 0.9716 and specificity 0.9482), the decision tree model 0.9434 (sensitivity 0.9615, specificity 0.9105), the neural network model 0.9502 (sensitivity 0.9628, specificity 0.9273), and the genetic algorithm model 0.9878 (sensitivity 1, specificity 0.9802). The accuracy of the genetic algorithm was significantly higher than the average predicted accuracy of 0.9612. The predicted outcome of the logistic regression model was higher than that of the neural network model but no significant difference was observed. The average predicted accuracy of the decision tree model was 0.9435 which was the lowest of all four predictive models. The standard deviation of the tenfold cross-validation was rather unreliable. This study indicated that the genetic algorithm model yielded better results than other data mining models for the analysis of the data of breast cancer patients in terms of the overall accuracy of the patient classification, the expression and complexity of the classification rule. The results showed that the genetic algorithm described in the present study was able to produce accurate results in the classification of breast cancer data and the classification rule identified was more acceptable and comprehensible.

Data mining, also known as Knowledge-Discovery in Databases (KDD), is the process of automatically searching large volumes of data for patterns. For instance, a clinical pattern might indicate a female who have diabetes or hypertension are easier suffered from stroke for 5 years in a future. Then, a physician can learn valuable knowledge from the data mining processes. Here, we present a study focused on the investigation of the application of artificial intelligence and data mining techniques to the prediction models of breast cancer. The artificial neural network, decision tree, logistic regression, and genetic algorithm were used for the comparative studies and the accuracy and positive predictive value of each algorithm were used as the evaluation indicators. 699 records acquired from the breast cancer patients at the University of Wisconsin, nine predictor variables, and one outcome variable were incorporated for the data analysis followed by the tenfold cross-validation. The results revealed that the accuracies of logistic regression model were 0.9434 (sensitivity 0.9716 and specificity 0.9482), the decision tree model 0.9434 (sensitivity 0.9615, specificity 0.9105), the neural network model 0.9502 (sensitivity 0.9628, specificity 0.9273), and the genetic algorithm model 0.9878 (sensitivity 1, specificity 0.9802). The accuracy of the genetic algorithm was significantly higher than the average predicted accuracy of 0.9612. The predicted outcome of the logistic regression model was higher than that of the neural network model but no significant difference was observed. The average predicted accuracy of the decision tree model was 0.9435 which was the lowest of all four predictive models. The standard deviation of the tenfold cross-validation was rather unreliable. This study indicated that the genetic algorithm model yielded better results than other data mining models for the analysis of the data of breast cancer patients in terms of the overall accuracy of the patient classification, the expression and complexity of the classification rule. The results showed that the genetic algorithm described in the present study was able to produce accurate results in the classification of breast cancer data and the classification rule identified was more acceptable and comprehensible.

Background and objective Electronic medical records with encoded entries should enhance the semantic interoperability of document exchange. However, it remains a challenge to encode the narrative concept and to transform the coded concepts into a standard entry-level document. This study aimed to use a novel approach for the generation of entry-level interoperable clinical documents. Methods Using HL7 clinical document architecture (CDA) as the example, we developed three pipelines to generate entry-level CDA documents. The first approach was a semi-automatic annotation pipeline (SAAP), the second was a natural language processing (NLP) pipeline, and the third merged the above two pipelines. We randomly selected 50 test documents from the i2b2 corpora to evaluate the performance of the three pipelines. Results The 50 randomly selected test documents contained 9365 words, including 588 Observation terms and 123 Procedure terms. For the Observation terms, the merged pipeline had a significantly higher F-measure than the NLP pipeline (0.89 vs 0.80, p Conclusions The combination of a semi-automatic annotation approach and the NLP application seems to be a solution for generating entry-level interoperable clinical documents.

The Clinical Document Architecture (CDA) is a widely adopted international standard for clinical documents design; the CDA document with coded entries increases the semantic interoperability in the course of clinical document exchange. This study developed a CDA entry generation pipeline for converting free-text documents into CDA documents with entry-level. The results showed that CDA entry coding could be accomplished using natural language processing.

This study developed and implemented a children's immunization management system with English and Traditional Chinese immunization ontology for semantic-based search of immunization knowledge. Parents and guardians are able to search vaccination-related information effectively. Jena Java Application Programming Interface (API) was used to search for synonyms and associated classes in this domain and then use them for searching by Google Search API. The searching results do not only contain suggested web links but also include a basic introduction to vaccine and related preventable diseases. Compared with the Google keyword-based search, over half of the 31 trial users prefer using semantic-based search of this system. Although the search runtime on this system is not as fast as well-known search engines such as Google or Yahoo, it can accurately focus on searching for child vaccination information to provide search results that better conform to the needs of users. Furthermore, the system is also one of the few health knowledge platforms that support Traditional Chinese semantic-based search.

This study provides epidemiologic data on the incidence of inguinal hernia repair in preschool children using the Taiwan National Health Insurance Research Database. We believe that the data on hernia repair in said database provide a close approximation of the true incidence of inguinal hernia in young children.A cohort of 1,073,891 deidentified individuals was randomly selected from an insured population of 23 million. Subjects born during the period 1997-2004 were followed from birth to 6 years. The chi-square test and logistic regression modeling were used for statistical analyses.A total of 92,308 individuals were born during the study period. Of these individuals, 3881 underwent hernia repairs. The cumulative incidence of hernia repair in children aged 0 to 6 years was 4.20%/7 years. The boy/girl ratio was 4.27:1 and the unilateral/bilateral ratio was 3.77:1. The incidence of hernia repair among boys was highest during the first year of life, but then decreased with age. In contrast, the incidence among girls remained stable during the first 6 years of life. Boys younger than 1 year had more bilateral repairs than boys in other age groups (p0.0001) and girls had significantly more bilateral repairs than boys (p0.0001). Subjects with a history of preterm birth also had a higher incidence of hernia repair than subjects who were born at full term (odds ratio=2.34, p0.0001).Yearly incidence of hernia repair was obtained from a nationwide database. Some of the observations have not been reported elsewhere.

Personal Health Record systems (PHRs) provide opportunities for patients to access their own PHR. However, PHRs are teeming with medical terminologies, such as disease and symptom names, etc. Patients need readily understandable and useful health knowledge in addition to their records in order to enhance their self-care ability. This study describes a Personal Health Record and Health Knowledge Sharing System (PHR&HKS) whereby users not only can maintain and import their PHR, but also can collate useful health Web resources that are related to their personal diseases. Furthermore, they can share the collated Web resources with any user with the same diseases and vice versa. To fulfill these objectives, IHE Cross-Enterprise Document Sharing (XDS) architecture was adopted to share and integrate the PHR. A registry ontology, consisting of part of the XDS document metadata attributes, the ICD-9-CM code, and part of the Dublin Core Metadata Element Set (DCMES), was created to enhance the health knowledge collating and sharing functions. The system was then tested and evaluated by 30 users. Among these individuals, 24 (81 %) held positive views on the ease of use and usefulness of the system while the remainder, who held either neutral (14 %) or negative (5 %) attitudes, were identified as individuals who were somewhat unwilling to maintain any PHR or share any information with others.

Population aging is an important issue in Taiwan. The percentage of elderly members of the population has been growing quickly during recent years. The requirements of long-term care for chronic and functionally impaired patients are rapidly increasing. Beside medical treatment, the needs of long-term care facilities are also continuously increasing. Among the variants of long-term care institutions, the quality of healthcare service varies widely and lacks an objective assessment indicator. In order to manage healthcare quality, Taiwan Long Term Care Association has defined six quality indicators including number of falls, nosocomial infections, use of physical restraints, non-planned weight gain or loss, pressure ulcers and non-planned transfers to acute hospitals. In order to facilitate, collect and calculate the indicators, the Association cooperates with us to implement a service system. The architecture of the system has four components including Data Collection Subsystem, Report Generation Subsystem, Aggregation and Analysis Subsystem and System Administration Subsystem. In this paper, we introduce and share our experience of the system's development.

Highlights? One of the major barriers for adopting Medical Information Systems in low resource setting is a lack of adequate network/communication infrastructure. ? Delay Tolerant Networking (DTN) is an approach for establishing network connectivity in situations where it is possible to support physical transport of the digital information. ? This study provides evidence of potential for DTN to support useful services that support health work in low resource settings as well as disclose the necessary frequency of data delivery for different services. ? Efforts to enhance awareness among stakeholders about how DTN can be used to support health services should be worthwhile. BackgroundMedical Informatics Systems (MIS) have been suggested as having great potential to improve health care delivery in low resource settings. One of the major barriers for adopting MIS in this context is a lack of adequate network/communication infrastructure. Delay Tolerant Networking (DTN) is an approach for establishing network connectivity in situations where it is possible to support physical transport of the digital information. To date most DTN research has been technically oriented, and very few services have been implemented to support healthcare systems using the technology. It is thus unclear about the potential that DTN may have for supporting MIS systems in low resource settings. The goals of the paper are twofold, first, to gain an initial estimate of interest in different services that can be supported by DTN. Second, to find out the necessary frequency associated with each service for supporting health work in low resource settings. MethodFifty questionnaires were distributed to attendants at the International Conference on Global Health that had acknowledged having health work experience in a poor connectivity context. The respondents were using a 5-point Likert scale regarding if 9 different potential DTN services "would be useful". They also were asked how often data delivery would be necessary for these services to be useful. The Chi square was calculated to measure acceptance. Results37 responses were received, aggregating the response rate of 74%. The respondents represented having work experience from 8 months to 15 years from 35 resource poor countries. The Chi square test showed very high statistical significance for "strongly agree and agree" for the potential usefulness of the proposed DTN services, with a p-value less than 0.001. The frequency of data delivery that would be necessary for services to be useful varied considerably. ConclusionThis study provides evidence of potential for DTN to support useful services that support health work in low resource settings, and that services like access to email, notification of lab results, backup of EHR and teleconsultation are seem to be most important services that can be supported by DTN. The necessary frequency of data delivery for each service, will be highly dependent on context. In a low resource setting with limited mobility, the physical transport of digital data at a frequency of less than once per week should still be sufficient for useful services like notification of lab results and ordering of medical supplies. Research comparing different methods for delivery of DTN data should thus be useful. Further research and collaboration between MIS and Computer Science research communities is recommended in order to help develop DTN services that can be evaluated. Efforts to enhance awareness among stakeholders about how DTN can be used to support health services should be worthwhile.

Information and communication technologies progress rapidly and many novel applications have been developed in many domains of human life. In recent years, the demand for healthcare services has been growing because of the increase in the elderly population. Consequently, a number of healthcare institutions have focused on creating technologies to reduce extraneous work and improve the quality of service. In this study, an information platform for tele- healthcare services was implemented. The architecture of the platform included a web-based application server and client system. The client system was able to retrieve the blood pressure and glucose levels of a patient stored in measurement instruments through Bluetooth wireless transmission. The web application server assisted the staffs and clients in analyzing the health conditions of patients. In addition, the server provided face-to-face communications and instructions through remote video devices. The platform deployed a service-oriented architecture, which consisted of HL7 standard messages and web service components. The platform could transfer health records into HL7 standard clinical document architecture for data exchange with other organizations. The prototyping system was pretested and evaluated in a homecare department of hospital and a community management center for chronic disease monitoring. Based on the results of this study, this system is expected to improve the quality of healthcare services.

Information and communication technologies progress rapidly and many novel applications have been developed in many domains of human life. In recent years, the demand for healthcare services has been growing because of the increase in the elderly population. Consequently, a number of healthcare institutions have focused on creating technologies to reduce extraneous work and improve the quality of service. In this study, an information platform for tele- healthcare services was implemented. The architecture of the platform included a web-based application server and client system. The client system was able to retrieve the blood pressure and glucose levels of a patient stored in measurement instruments through Bluetooth wireless transmission. The web application server assisted the staffs and clients in analyzing the health conditions of patients. In addition, the server provided face-to-face communications and instructions through remote video devices. The platform deployed a service-oriented architecture, which consisted of HL7 standard messages and web service components. The platform could transfer health records into HL7 standard clinical document architecture for data exchange with other organizations. The prototyping system was pretested and evaluated in a homecare department of hospital and a community management center for chronic disease monitoring. Based on the results of this study, this system is expected to improve the quality of healthcare services.

Electronic Health Records (EHR) allow health records to be easily integrated, shared, and exchanged between health institutions. However, the system heterogeneity and the lack of standards present current challenges which hinder efficient exchange of medical information between institutions. Health Level 7 (HL7) Clinical Document Architecture (CDA) is a renowned exchange standard. Presently, there are many studies regarding CDA to resolve the problems discussed above. This paper implements a tool to simplify the CDA generation and provide a friendly user interface. It is divided into three main parts: Template Designer, Mapping engine and CDA Generator. The CDA tool helps users to create and edit the template (e.g. discharge summary) through an efficient user interface. In addition, it maps the CDA elements to the field of the legacy information table. Ultimately, the CDA generator generates the CDA documents and saves them as XML files. The application of CDA as a promising standard in the hospital environment ensures the efficient exchange of health records.

Duplicate medication between enterprises not only wastes medical resources, but also endangers a patient's safety. However, medication information may come from different medical institutes and use different terminologies. The majority of patients do not know the names of these drugs; and for other doctors, they may also not understand medication information content. In order to avoid duplicate medication, drug allergy or other drug incidents, this study hope to implement web services for sharing medication information between enterprises base on IHE Cross-Enterprise Document Sharing (XDS) and RxNorm, making the medication information correspond to standardization. Through the consistency of these standards, the doctor could refer to a complete medication record of the patient in the past when prescribing in order to avoid the duplication of a drug. Because patients in recent years care more about their own health management consciousness, this service also provides automatic duplicate medication check.

The emergency department (ED) represents an important transition of care for patients, where understanding the patient health condition is critical to forming an appropriate plan of care. Because many emergency referral visits are unplanned and urgent, this information may not be conveyed in advance to ED physicians. Loss of this data can lead to costly over-testing in the ED referral, or worse, an inappropriate disposition for the patient. In this study, we follow the XDS-MS (Cross-Enterprise Sharing of Medical Summaries) profile and present methods to implement a prototype of an Emergency department system for referral documents. The system will not only retrieve the patient's critical medical information, but also generate the XML-based CDA R2 document automatically. The CDA document can be shared among different hospital emergency departments immediately. It can not only facilitate the referral process but also solve the problem of patient's health information loses and facilitate sharing across different emergency departments.

The Integrating the Healthcare Enterprise (IHE) provided a series profiles for decreasing costs and integrating different healthcare enterprises. In IHE Cross-Enterprise Document Sharing for Image (XDS-I), it provides a web service and Web Access to Digital Imaging and Communication in Medicine Persistent Objects (WADO) service, to access DICOM objects. This paper analyzes the operational specifications of the WADO service and extends the access capability of WADO service to get other related documents and/or reports based on IHE XDS-I profile. And then we re-design an ontology following the Unified Medical Language System (UMLS) for use in the relationship between Structure Reports (SR). By analyzing the SR to retrieve key words and using an ontology to search, we can retrieve the other related reports and/or images. This research wants to find the related medical image documents when getting a SR or DICOM image from a patient or other Enterprises. It can reduce the time for searching and improve the diagnostic accuracy, when radiologist write or read reports and images.