Information Retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Computer Methods and Programs in Biomedicine
Active online classification via information maximization
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Clinical Decision Support (CDS) tools are typically designed to assist physicians in clinical decision making at Point Of Care (POC). Existing CDS tools commonly rely on relatively simple rules, deduced from relevant clinical guidelines. However, the increasing pace by which Health Care Organizations (HCOs) adopt Electronic Health Record technologies suggest great potential for CDS tools that directly mine the massive clinical data collected at the HCO. A natural goal for such tools is to exploit Machine Learning (ML) algorithms in order to predict patient's outcome. However, the technical challenges involved in constructing such a system in practice are quite involved, where in particular treatments outcome are often not available as part of the HCO's data. Here, we propose a different strategy in which we use the assigned treatments as the labels in the learning process of the supervised ML algorithms. We present two different use-cases in which our approach could be used. First, in order to highlight the clinical features most associated with the assigned treatments; and second, in order to predict the customary treatment for a patient at POC in a statistically data-driven manner. Altogether, our approach represents a novel strategy that is complementary to the classical paradigm of rule-based clinical guidelines adherence. Experimental results over hypertension clinical data demonstrate the validity of our approach.