title: PSF: A Unified Patient Similarity Evaluation Framework Through Metric Learning With Weak Supervision.
abstract: 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.
authors.lastname: Fei Wang
authors.forename: Jimeng
authors.initials: J
authors.lastname: Sun
mesh_terms: D000368:Aged; D000465:Algorithms; D020000:Decision Support Systems, Clinical; D005246:Feedback; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D008875:Middle Aged; D010820:Physicians; D011379:Prognosis; D012044:Regression Analysis