Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records.

Intelligent models for predicting hemodialysis-related complications, i.e., hypotension and the deterioration of the quality or obstruction of the AV fistula, based on machine learning (ML) methods were established to offer early warnings to medical staff and give them enough time to provide pre-emptive treatment. A novel integration platform collected data from the Internet of Medical Things (IoMT) at a dialysis center and inspection results from electronic medical records (EMR) to train ML algorithms and build models. The selection of the feature parameters was implemented using Pearson's correlation method. Then, the eXtreme Gradient Boost (XGBoost) algorithm was chosen to create the predictive models and optimize the feature choice. 75% of collected data are used as a training dataset and the other 25% are used as a testing dataset. We adopted the prediction precision and recall rate of hypotension and AV fistula obstruction to measure the effectiveness of the predictive models. These rates were sufficiently high at approximately 71%-90%. In the context of hemodialysis, hypotension and the deterioration of the quality or obstruction of the arteriovenous (AV) fistula affect treatment quality and patient safety and may lead to a poor prognosis. Our prediction models with high accuracies can provide excellent references and signals for clinical healthcare service providers. Clinical and Translational Impact Statement-With the integrated dataset collected from IoMT and EMR, the superior predictive results of our models for complications of hemodialysis patients are demonstrated. We believe, after enough clinical tests are implemented as planned, these models can assist the healthcare team in making appropriate preparations in advance or adjusting the medical procedures to avoid these adverseevents.

Efficient Clinical Data Sharing Framework Based on Blockchain Technology.

BACKGROUND: While electronic health records have been collected for many years in Taiwan, their interoperability across different health care providers has not been entirely achieved yet. The exchange of clinical data is still inefficient and time consuming. OBJECTIVES: This study proposes an efficient patient-centric framework based on the blockchain technology that makes clinical data accessible to patients and enable transparent, traceable, secure, and effective data sharing between physicians and other health care providers. METHODS: Health care experts were interviewed for the study, and medical data were collected in collaboration with Ministry of Health and Welfare (MOHW) Chang-Hua hospital. The proposed framework was designed based on the detailed analysis of this information. The framework includes smart contracts in an Ethereum-based permissioned blockchain to secure and facilitate clinical data exchange among different parties such as hospitals, clinics, patients, and other stakeholders. In addition, the framework employs the Logical Observation Identifiers Names and Codes (LOINC) standard to ensure the interoperability and reuse of clinical data. RESULTS: The prototype of the proposed framework was deployed in Chang-Hua hospital to demonstrate the sharing of health examination reports with many other clinics in suburban areas. The framework was found to reduce the average access time to patient health reports from the existing next-day service to a few seconds. CONCLUSION: The proposed framework can be adopted to achieve health record sharing among health care providers with higher efficiency and protected privacy compared to the system currently used in Taiwan based on the client-server architecture.

An Ensemble Classifier with Case-Based Reasoning System for Identifying Internet Addiction.

Internet usage has increased dramatically in recent decades. With this growing usage trend, the negative impacts of Internet usage have also increased significantly. One recurring concern involves users with Internet addiction, whose Internet usage has become excessive and disrupted their lives. In order to detect users with Internet addiction and disabuse their inappropriate behavior early, a secure Web service-based EMBAR (ensemble classifier with case-based reasoning) system is proposed in this study. The EMBAR system monitors users in the background and can be used for Internet usage monitoring in the future. Empirical results demonstrate that our proposed ensemble classifier with case-based reasoning (CBR) in the proposed EMBAR system for identifying users with potential Internet addiction offers better performance than other classifiers.

Electronic hand-drafting and picture management system.

The Department of Health of Executive Yuan in Taiwan (R.O.C.) is implementing a five-stage project entitled Electronic Medical Record (EMR) converting all health records from written to electronic form. Traditionally, physicians record patients' symptoms, related examinations, and suggested treatments on paper medical records. Currently when implementing the EMR, all text files and image files in the Hospital Information System (HIS) and Picture Archiving and Communication Systems (PACS) are kept separate. The current medical system environment is unable to combine text files, hand-drafted files, and photographs in the same system, so it is difficult to support physicians with the recording of medical data. Furthermore, in surgical and other related departments, physicians need immediate access to medical records in order to understand the details of a patient's condition. In order to address these problems, the Department of Health has implemented an EMR project, with the primary goal of building an electronic hand-drafting and picture management system (HDP system) that can be used by medical personnel to record medical information in a convenient way. This system can simultaneously edit text files, hand-drafted files, and image files and then integrate these data into Portable Document Format (PDF) files. In addition, the output is designed to fit a variety of formats in order to meet various laws and regulations. By combining the HDP system with HIS and PACS, the applicability can be enhanced to fit various scenarios and can assist the medical industry in moving into the final phase of EMR.