A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models.

Journal: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
PMID: 35227445

Abstract

OBJECTIVES: We propose a framework of health outcomes modeling with dynamic decision making and real-world data (RWD) to evaluate the potential utility of novel risk prediction models in clinical practice. Lung transplant (LTx) referral decisions in cystic fibrosis offer a complex case study.

Authors

  • Patricia J Rodriguez
    The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington, Seattle, WA, USA. Electronic address: prodrig@uw.edu.
  • David L Veenstra
    The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington, Seattle, WA, USA.
  • Patrick J Heagerty
    Department of Biostatistics, University of Washington, Seattle, WA, USA.
  • Christopher H Goss
    Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA; Division of Pulmonology, Department of Pediatrics, University of Washington, Seattle, WA, USA.
  • Kathleen J Ramos
    Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA.
  • Aasthaa Bansal
    The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington, Seattle, WA, USA. Electronic address: abansal@uw.edu.

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