Probabilistic Machine Learning for Healthcare.

Journal: Annual review of biomedical data science
Published Date:

Abstract

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial, including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.

Authors

  • Irene Y Chen
    Computational Precision Health, University of California San Francisco, San Francisco, CA, University of California Berkeley, Berkeley, CA.
  • Shalmali Joshi
    Vector Institute, Toronto, Ontario, Canada.
  • Marzyeh Ghassemi
    Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States.
  • Rajesh Ranganath
    Department of Computer Science, New York University.