Abstract
BackgroundIn the era of information explosion, the use of the internet to assist with clinical practice and diagnosis has become a cutting-edge area of research. The application of medical informatics allows patients to be aware of their clinical conditions, which may contribute toward the prevention of several chronic diseases and disorders.ObjectiveIn this study, we applied machine learning techniques to construct a medical database system from electronic medical records (EMRs) of subjects who have undergone health examination. This system aims to provide online self-health evaluation to clinicians and patients worldwide, enabling personalized health and preventive health.MethodsWe built a medical database system based on the literature, and data preprocessing and cleaning were performed for the database. We utilized both supervised and unsupervised machine learning technology to analyze the EMR data to establish prediction models. The models with EMR databases were then applied to the internet platform.ResultsThe validation data were used to validate the online diagnosis prediction system. The accuracy of the prediction model for metabolic syndrome reached 91%, and the area under the receiver operating characteristic (ROC) curve was 0.904 in this system. For chronic kidney disease, the prediction accuracy of the model reached 94.7%, and the area under the ROC curve (AUC) was 0.982. In addition, the system also provided disease diagnosis visualization via clustering, allowing users to check their outcome compared with those in the medical database, enabling increased awareness for a healthier lifestyle.ConclusionsOur web-based health care machine learning system allowed users to access online diagnosis predictions and provided a health examination report. Users could understand and review their health status accordingly. In the future, we aim to connect hospitals worldwide with our platform, so that health care practitioners can make diagnoses or provide patient education to remote patients. This platform can increase the value of preventive medicine and telemedicine.
Highlights
In the ever-changing technological era, the internet can provide rapid and convenient medical services in the form of health care, preventive medicine, and telemedicine
To construct a health care information system, several factors must be considered: the hospital information system, including both clinical management and diagnosis services; the storage and processing of patient information, such as electronic medical records (EMRs) and electronic health records; decision support systems, such as expert diagnosis systems; and the artificial intelligence (AI) algorithms that need to be applied to those factors [5,6,7,8]
The web-based health care machine learning (ML) system provides online diagnosis of three diseases (Figure 3), and it is available on the internet [53]
Summary
In the ever-changing technological era, the internet can provide rapid and convenient medical services in the form of health care, preventive medicine, and telemedicine. Objective: In this study, we applied machine learning techniques to construct a medical database system from electronic medical records (EMRs) of subjects who have undergone health examination. This system aims to provide online self-health evaluation to clinicians and patients worldwide, enabling personalized health and preventive health. Methods: We built a medical database system based on the literature, and data preprocessing and cleaning were performed for the database We utilized both supervised and unsupervised machine learning technology to analyze the EMR data to establish prediction models. This platform can increase the value of preventive medicine and telemedicine
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