Ardavan Afshar, Ioakeim Perros, Haesun Park, Christopher Defilippi, Xiaowei Yan, Walter Stewart, Joyce Ho, Jimeng Sun

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


Phenotyping electronic health records (EHR)focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of patients in those groups. Tensor factorization has been an effective tool for phenotyping. Most of the existing works assume either a static patient representation with aggregate data or only model temporal data. However, real EHR data contain both temporal (e.g., longitudinal clinical visits) and static information (e.g., patient demographics), which are difficult to model simultaneously. In this paper, we propose Temporal And Static TEnsor factorization (TASTE) that jointly models both static and temporal information to extract phenotypes.TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor. To fit the proposed model, we transform the original problem into simpler ones which are optimally solved in an alternating fashion. For each of the sub-problems, our proposed mathematical re-formulations lead to efficient sub-problem solvers. Comprehensive experiments on large EHR data from a heart failure (HF) study confirmed that TASTE is up to 14× faster than several baselines and the resulting phenotypes were confirmed to be clinically meaningful by a cardiologist. Using 60 phenotypes extracted by TASTE, a simple logistic regression can achieve the same level of area under the curve (AUC) for HF prediction compared to a deep learning model using recurrent neural networks (RNN) with 345 features.

Original languageEnglish (US)
Title of host publicationACM CHIL 2020 - Proceedings of the 2020 ACM Conference on Health, Inference, and Learning
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Electronic)9781450370462
StatePublished - Feb 4 2020
Externally publishedYes
Event2020 ACM Conference on Health, Inference, and Learning, CHIL 2020 - Toronto, Canada
Duration: Apr 2 2020Apr 4 2020

Publication series

NameACM CHIL 2020 - Proceedings of the 2020 ACM Conference on Health, Inference, and Learning


Conference2020 ACM Conference on Health, Inference, and Learning, CHIL 2020


  • Computational Phenotyping
  • Predictive modeling
  • Tensor Factorization

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Education
  • Health(social science)


Dive into the research topics of 'TASTE'. Together they form a unique fingerprint.

Cite this