PSF: A unified Patient similarity evaluation framework through metric learning with weak supervision

Fei Wang, Jimeng Sun

Research output: Contribution to journalArticlepeer-review

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 distancemetric 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.

Original languageEnglish (US)
Article number7091853
Pages (from-to)1053-1060
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Volume19
Issue number3
DOIs
StatePublished - May 1 2015
Externally publishedYes

Keywords

  • Health informatics
  • Metric learning
  • Patient similarity

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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