| アブストラクト | Industrial reforms utilizing artificial intelligence (AI) have been progressing remarkably worldwide in recent years. In medical informatics, medical big-data analytics involving AI are increasingly being promoted, and AI in the medical field is being widely applied in research areas such as protein-structure analysis and diagnostic support. Previously, we developed a unique adverse drug reactions analysis system that incorporates Accord.NET, an open-source machine learning (ML) framework written in the programming language C#, and uses the Japanese Adverse Drug Event Report (JADER) database. The developed system can provide necessary information for exploratory investigation of drug efficacy, side effects, adherence, and so on. To efficiently interpret the calculated data and minimize noise, the developed system features a data visualization tool that can visualize the results of various statistical analyses and machine learning models in real-time three dimensions (3D), making it intuitive to grasp the results. This feature makes the system ideal for individuals in clinical work. We believe that the system will facilitate more efficient drug management and clinical pharmacy research. In this review, we introduce an example of domain-driven design development of this AI analysis system for pharmacists in clinical practice with the aim of further utilizing medical big data and AI analytics. |
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