Designing a Clinical Decision Support System (CDSS) with AI and C#
Building an AI-Powered Clinical Decision Support System with C# and
Summary
This article provides a technical guide on building an Artificial Intelligence-powered Clinical Decision Support System (CDSS). By analyzing EHR data and patient information, the system aims to enhance medical decision-making by providing diagnostic support and treatment planning recommendations.
Details
Clinical Decision Support Systems (CDSS) are essential tools that analyze vast amounts of data within Electronic Health Records (EHRs) to provide prompts and alerts based on evidence-based clinical guidelines. This article details the methodology for designing and implementing such a system using C# and .NET. The proposed architecture is hybrid, utilizing ASP.NET Core for the UI, FHIR APIs for the data layer, an AI engine (ONNX/ML.NET), and a multi-database backend comprising DynamoDB (session logs), MongoDB (clinical notes), and Amazon Redshift (analytics). Key steps include reading patient and observation data via standardized FHIR resources and mapping this information to local input structures, providing a highly practical technical tutorial.
Technology Note
FHIR（Fast Healthcare Interoperability Resources）は医療データ交換の国際標準。このエントリの関連技術: FHIR
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