Granite: Diversified, Sparse Tensor Factorization for Electronic Health Record-Based Phenotyping

Jette Henderson, Joyce C. Ho, Abel N. Kho, Joshua C. Denny, Bradley A. Malin, Jimeng Sun, Joydeep Ghosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

One of the most formidable challenges electronic health records (EHRs) pose for traditional analytics is the inability to map directly (or reliably) to medical concepts or phenotypes. Among other things, EHR-based phenotyping can help identify and target patients for interventions and improve real-time clinical decisions. Existing phenotyping approaches often require labor-intensive supervision from medical experts or do not focus on generating concise and diverse phenotypes. Sparsity in phenotypes is key to making them interpretable and useful to clinicians, while diversity allows clinicians to grasp the main features of a patient population quickly.In this paper, we introduce Granite, a diversified, sparse nonnegative tensor factorization method to derive phenotypes with limited human supervision. Compared to existing high-throughput phenotyping techniques, Granite yields phenotypes with much more distinct (non-overlapping) elements that can, as an artifact, capture rare phenotypes. Moreover, the resulting concise phenotypes retain predictive powers comparable to or surpassing existing dimensionality reduction techniques. We evaluate Granite by comparing its resulting phenotypes with those generated using state-of-the-art, high-throughput methods on simulated as well as real EHR data. Our algorithm offers a promising and novel data-driven solution to rapidly characterize, predict, and manage a wide range of diseases.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
EditorsMollie Cummins, Julio Facelli, Gerrit Meixner, Christophe Giraud-Carrier, Hiroshi Nakajima
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages214-223
Number of pages10
ISBN (Electronic)9781509048816
DOIs
StatePublished - Sep 8 2017
Event5th IEEE International Conference on Healthcare Informatics, ICHI 2017 - Park City, United States
Duration: Aug 23 2017Aug 26 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017

Other

Other5th IEEE International Conference on Healthcare Informatics, ICHI 2017
CountryUnited States
CityPark City
Period8/23/178/26/17

Keywords

  • Computational phenotyping
  • Data mining
  • Electronic health records
  • Feature extraction
  • Health information management
  • Tensor factorization

ASJC Scopus subject areas

  • Health Informatics

Fingerprint Dive into the research topics of 'Granite: Diversified, Sparse Tensor Factorization for Electronic Health Record-Based Phenotyping'. Together they form a unique fingerprint.

Cite this