PhenoTree: Interactive Visual Analytics for Hierarchical Phenotyping From Large-Scale Electronic Health Records

Abstract

Electronic health records (EHRs) capture comprehensive patient information in digital form from a variety of sources. Increasing availability of EHRs has facilitated development of data and visual analytic tools for healthcare analytics, such as clinical decision support and patient care management systems. Many healthcare analytic tools are used to investigate fundamental problems, such as study of patient population, exploring complicated interactions among patients and their medical histories, and extracting structured phenotypes characterizing the patient population. In this paper, we propose PhenoTree , a novel data-driven, hierarchical, and interactive phenotyping tool, that enables physicians and medical researchers to participate in the phenotyping process of large-scale EHR cohorts. The proposed visual analytic tool allows users to interactively explore EHR cohorts, and generate, interpret, evaluate, and refine phenotypes by building and navigating a phenotype hierarchy. Specifically, given a cohort or subcohort, PhenoTree employs sparse principal component analysis (SPCA) to identify key clinical features that characterize the population. The clinical features provide a natural way to generate deeper phenotypes at finer granularities by expanding the phenotype hierarchy. To facilitate the intensive computation required for interactive analytics, we design an efficient SPCA solver based on a variance reduced stochastic gradient technique. The benefits of our method are demonstrated by analyzing two different EHR patient cohorts, a public and a private dataset containing EHRs of $101\,767$ and $223\,076$ patients, respectively. Our evaluations show that PhenoTree can detect clinically meaningful hierarchical phenotypes.