Phenotyping through Semi-Supervised Tensor Factorization (PSST)

• Type of Material: Article
• Authors: Jetter Henderson, University of Texas at Austin; He He, Emory University; Bradley A. Malin, Vanderbilt University; Joshua C. Denny, Vanderbilt University; Abel N. Kho, Northwestern University; Joydeep Ghosh, University of Texas at Austin; Joyce C. Ho, Emory University
• Language: English
• Date: 2018-01-01
• Publisher: AMIA
• Publication Version: Final Published Version
• Copyright Statement: ©2018 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose.
• Final Published Version (URL): https://knowledge.amia.org/
• Title of Journal or Parent Work: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
• ISSN: 1942-597X
• Volume: 2018
• Start Page: 564
• End Page: 573
• Grant/Funding Information: This work was supported by NSF grants 1418511, 1417819,and 1418504.
• Research Categories: Computer Science

Abstract

A computational phenotype is a set of clinically relevant and interesting characteristics that describe patients with a given condition. Various machine learning methods have been proposed to derive phenotypes in an automatic, high-throughput manner. Among these methods, computational phenotyping through tensor factorization has been shown to produce clinically interesting phenotypes. However, few of these methods incorporate auxiliary patient information into the phenotype derivation process. In this work, we introduce Phenotyping through Semi-Supervised Tensor Factorization (PSST), a method that leverages disease status knowledge about subsets of patients to generate computational phenotypes from tensors constructed from the electronic health records of patients. We demonstrate the potential of PSST to uncover predictive and clinically interesting computational phenotypes through case studies focusing on type-2 diabetes and resistant hypertension. PSST yields more discriminative phenotypes compared to the unsupervised methods and more meaningful phenotypes compared to a supervised method.