Characterizing Patient Representations for Computational Phenotyping.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium
Published Date: Apr 29, 2023

Abstract

Patient representation learning methods create rich representations of complex data and have potential to further advance the development of computational phenotypes (CP). Currently, these methods are either applied to small predefined concept sets or all available patient data, limiting the potential for novel discovery and reducing the explainability of the resulting representations. We report on an extensive, data-driven characterization of the utility of patient representation learning methods for the purpose of CP development or automatization. We conducted ablation studies to examine the impact of patient representations, built using data from different combinations of data types and sampling windows on rare disease classification. We demonstrated that the data type and sampling window directly impact classification and clustering performance, and these results differ by rare disease group. Our results, although preliminary, exemplify the importance of and need for data-driven characterization in patient representation-based CP development pipelines.

Authors

  • Tiffany J Callahan
    Computational Bioscience Program and Department of Pharmacology, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado 80045, USA.
  • Adrianne L Stefanksi
    University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
  • Danielle M Ostendorf
    University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
  • Jordan M Wyrwa
    University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
  • Sara J Deakyne Davies
    Children's Hospital Colorado, Aurora, CO, 80045, USA.
  • George Hripcsak
    Department of Biomedical Informatics, Columbia University, 622 W 168th Street, PH20, New York, NY 10032, USA; Medical Informatics Services, NewYork-Presbyterian Hospital, 622 W 168th Street, PH20, New York, NY 10032, USA. Electronic address: hripcsak@columbia.edu.
  • Lawrence E Hunter
    Computational Bioscience Program and Department of Pharmacology, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado 80045, USA.
  • Michael G Kahn
    University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.

Keywords

External Resources

Popular Topics
Recent Journals