Supervised patient similarity measure of heterogeneous patient records

Abstract

Patient similarity assessment is an important task in the context of patient cohort identif cation for comparative effectiveness studies and clinical decision support applications. The goal is to derive clinically meaningful distance metric to measure the similarity between patients represented by their key clinical indicators. How to incorporate physician feedback with regard to the retrieval results? How to interactively update the underlying similarity measure based on the feedback? Moreover, often different physicians have different understandings of patient similarity based on their patient cohorts. The distance metric learned for each individual physician often leads to a limited view of the true underlying distance metric. How to integrate the individual distance metrics from each physician into a globally consistent unif ed metric? We describe a suite of supervised metric learning approaches that answer the above questions. In particular, we present Locally Supervised Metric Learning (LSML) to learn a generalized Mahalanobis distance that is tailored toward physician feedback. Then we describe the interactive metric learning (iMet) method that can incrementally update an existing metric based on physician feedback in an online fashion. To combine multiple similarity measures from multiple physicians, we present Composite Distance Integration (Comdi) method. In this approach we f rst construct discriminative neighborhoods from each individual metrics, then combine them into a single optimal distance metric. Finally, we present a clinical decision support prototype system powered by the proposed patient similarity methods, and evaluate the proposed methods using real EHR data against several baselines.