Validation of an ontological medical decision support system for patient treatment using a repository of patient data: Insights into the value of machine learning

In this article, we begin by presenting OMeD, a medical decision support system, and argue for its value over purely probabilistic approaches that reason about patients for time-critical decision scenarios. We then progress to present Holmes, a Hybrid Ontological and Learning MEdical System which supports decision making about patient treatment. This system is introduced in order to cope with the case of missing data. We demonstrate its effectiveness by operating on an extensive set of real-world patient health data from the CDC, applied to the decision-making scenario of administering sleeping pills. In particular, we clarify how the combination of semantic, ontological representations, and probabilistic reasoning together enable the proposal of effective patient treatments. Our focus is thus on presenting an approach for interpreting medical data in the context of real-time decision making. This constitutes a comprehensive framework for the design of medical recommendation systems for potential use by medical professionals and patients both, with the end result being personalized patient treatment. We conclude with a discussion of the value of our particular approach for such diverse considerations as coping with misinformation provided by patients, performing effectively in time-critical environments where real-time decisions are necessary, and potential applications facilitating patient information gathering.