A comparative evaluation of an ontological medical decision support system (OMeD) for critical environments

Modern medical decision making systems require users to manually collect and process information from distributed and heterogeneous repositories to facilitate the decision making process. There are many factors (such as time, volume of information and technical ability) that can potentially compromise the quality of decisions made for patients. In this work we demonstrate and evaluate a new medical decision making support 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 techniques 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 provide 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.