Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Patient Mortality
Interpretable AI models may serve as a decision aid to improve mortality in patients presenting with a blunt splenic injury. Our data-driven prescriptive OPT models may aid in prescribing the appropriate management in this patient cohort based on their characteristics.
Authors
- Azadeh Tabari (ORCID: https://orcid.org/0000-0002-5685-6401)
- Dimitris Bertsimas (ORCID: https://orcid.org/0000-0002-1985-1003)
- Ikemsinachi C. Nzenwa (ORCID: https://orcid.org/0000-0002-1131-5505)
- Haytham M.A. Kaafarani (ORCID: https://orcid.org/0000-0002-4682-9135)
- Dania Daye (ORCID: https://orcid.org/0000-0002-2230-7199)
- George C. Velmahos (ORCID: https://orcid.org/0000-0001-5459-0374)
- Vahe S Panossian (ORCID: https://orcid.org/0000-0001-8085-1021)
- Bolin Song (ORCID: https://orcid.org/0009-0005-6108-4695)
- Jefferson A. Proaño-Zamudio (ORCID: https://orcid.org/0000-0001-8970-4642)
- Yu Ma (ORCID: https://orcid.org/0000-0001-8409-7222)
- Veerle P. C. van Zon
Institutions
- Harvard University (US)
- Massachusetts General Hospital (US)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Bioengineering
- Published
- 2025-03-24
- DOI
- https://doi.org/10.3390/bioengineering12040336
- Citations
- 1
- Primary Topic
- Abdominal Trauma and Injuries
- Type
- article
- Field-Weighted Citation Impact
- 2.01