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Relative sparsity for medical decision problems.

Samuel J Weisenthal1,2, Sally W Thurston1, Ashkan Ertefaie1

  • 1Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York.

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|June 14, 2023
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Summary
This summary is machine-generated.

This study introduces a novel method for creating interpretable data-driven healthcare policies. It ensures new policies minimally differ from standard care, aiding provider and patient understanding.

Keywords:
causal inferenceindividualized medicinelassoreinforcement learningtrust region policy optimization

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Area of Science:

  • Machine Learning
  • Healthcare Analytics
  • Causal Inference

Background:

  • Data-driven policies offer potential for improved healthcare decision-making.
  • Explaining policy changes to healthcare providers and patients is crucial for adoption.
  • Current methods lack interpretability regarding policy differences from standard care.

Purpose of the Study:

  • To develop a method for estimating interpretable data-driven healthcare policies.
  • To ensure new policies are sparse in their differences from the standard of care.
  • To facilitate the adoption of data-driven decision aids in clinical practice.

Main Methods:

  • Adaptation of Trust Region Policy Optimization (TRPO) principles.
  • Introduction of "relative sparsity" to control policy parameter differences.
  • Development of a criterion for selecting a tuning parameter to balance policy change and interpretability.

Main Results:

  • Demonstrated ability to control the number of differing policy parameters.
  • Successful application to an observational healthcare dataset.
  • Derived a policy that is easily explainable relative to the standard of care.

Conclusions:

  • The proposed method enhances the interpretability of data-driven policies.
  • Relative sparsity promotes the adoption of new decision-making strategies in healthcare.
  • This approach can improve health outcomes by facilitating the use of advanced analytics.