A Framework for AI-Based Clinical Decision Support that is Patient-Centric and Evidence-Based

In this paper, we present a framework which enables medical decision making in the presence of partial information. At the core is ontology-based automated reasoning; this is augmented with machine learning techniques to enhance existing patient datasets. Our approach supports interoperability between different health information systems. This is clarified in a sample implementation that combines three separate datasets (patient data, drug drug interactions and drug prescription rules) to demonstrate the effectiveness of our algorithms in producing effective medical decisions. In short, we demonstrate the potential for artificial intelligence to support a task where there is a critical need from medical professionals, coping with missing or noisy patient data and enabling the usage of multiple medical datasets.

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