A Comparative Evaluation of an Ontological Medical Decision Support System (OMeD) for Critical Environments This research was funded in part by the National Science and Engineering Research Council of Canada (NSERC). John A. Doucette, Atif Khan, and Robin Cohen David R. Cheriton School of Computer Science, University of Waterloo 200 University Avenue West Waterloo, ON, Canada {j3doucet,atif.khan,rcohen}@cs.uwaterloo.ca January 16, 2014 Abstract Modern medical decision making systems require users to manually collect and process information from distributed and heterogeneous repos- itories to facilitate the decision making process. There are many factors (such as time, volume of information and technical ability) that can po- tentially compromise the quality of decisions made for patients. In this work we demonstrate and evaluate a new medical decision making sup- port system, called OMeD, which automatically answers medical queries in real time, by collecting and processing medical information. OMeD utilizes a natural-language-like user interface (for querying) and semantic web techniques (for knowledge representation and reasoning) to answer queries. We compare OMeD to a set of standard machine learning tech- niques across a series of benchmarks based on simulated patient data. The conventional techniques attempt to learn the answer to a query by analyzing simulated patient records. The sparsity of the simulated data leads conventional techniques to frequently misidentify the relationships between medical concepts. In contrast, OMeD is able to reliably pro- vide correct answers to queries. Unlike conventional automated decision support systems, OMeD also generates independently verifiable proofs for its answers, providing healthcare workers with confidence in the system’s recommendations. 1