Continuous-Time Bayesian Modeling of Clinical Data

Inference from hospital patient records is difficult because data collection is done at arbitrary (not evenlyspaced) time intervals, and key clinical information is recorded only in unstructured form (as free text in doctors’ notes). We present remind, a framework for performing inference from patient records based upon continuous-time Markov models and Bayesian networks. We empirically justify the need for such a complex model. remind only uses easily-available domain knowledge which we obtain from physicians and medical literature, which may be inaccurate. We also demonstrate the robustness of our approach to parameter assignment.