Clinical decision making under uncertainty: a bootstrapped counterfactual inference approach
This study demonstrates the effectiveness of combining bootstrap and adversarial learning techniques in policy learning for clinical decision support. It not only enhances the accuracy and reliability of policy evaluation and optimization but also paves avenues for leveraging advanced counterfactual machine learning in healthcare.
Authors
- May D. Wang (ORCID: https://orcid.org/0000-0003-3961-3608)
- Wenqi Shi (ORCID: https://orcid.org/0000-0001-8972-7342)
- Anirudh Choudhary
- Hang Wu
Institutions
- Georgia Institute of Technology (US)
- The Wallace H. Coulter Department of Biomedical Engineering (US)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-09-28
- DOI
- https://doi.org/10.1186/s12911-024-02606-z
- Citations
- 7
- Primary Topic
- Health Systems, Economic Evaluations, Quality of Life
- Type
- article
- Field-Weighted Citation Impact
- 7.90
Funders
- Microsoft Research
- Parker H. Petit Institute for Bioengineering and Bioscience