Continuous-time Bayesian modeling of clinical data
Sathyakama Sandilya, R. Bharat Rao · 2004
Inference from hospital patient records is difficult because data collection is done at arbitrary (not evenly-spaced) 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.