PSF: A Unified Patient Similarity Evaluation Framework Through Metric Learning With Weak Supervision

Patient similarity is an important analytic operation in healthcare applications. At the core, patient similarity takes an index patient as the input and retrieves a ranked list of similar patients that are relevant in a specific clinical context. It takes patient information such as their electronic health records as input and computes the distance between a pair of patients based on those information. To construct a clinically valid similarity measure, physician input often needs to be incorporated. However, obtaining physicians' input is difficult and expensive. As a result, typically only limited physician feedbacks can be obtained on a small portion of patients. How to leverage all unlabeled patient data and limited supervision information from physicians to construct a clinically meaningful distance metric? In this paper, we present a patient similarity framework (PSF) that unifies and significantly extends existing supervised patient similarity metric learning methods. PSF is a general framework that can learn an appropriate distance metric through supervised and unsupervised information. Within PSF framework, we propose a novel patient similarity algorithm that uses local spline regression to capture the unsupervised information. To speedup the incorporation of physician feedback or newly available clinical information, we introduce a general online update algorithm for an existing PSF distance metric.