Development and Validation of a Hybrid Machine Learning Model to Predict Lung Transplant Outcomes
In this large prognostic study, the interpretable hybrid model provided practical, personalized risk stratification for lung transplant outcomes. With moderate discrimination, good calibration, and clear clinical utility, the model supports shared decision-making and is accessible via a web-based calculator.
Authors
- Vaidehi Kaza (ORCID: https://orcid.org/0000-0003-3578-6077)
- Srdjan Lesaja (ORCID: https://orcid.org/0000-0002-4225-1954)
- Matthias Peltz (ORCID: https://orcid.org/0000-0002-7384-252X)
- Vineet Kumar Kamal (ORCID: https://orcid.org/0000-0002-7295-1644)
- Jonathan D. Plasencia (ORCID: https://orcid.org/0000-0002-8878-2259)
- J. Murala (ORCID: https://orcid.org/0000-0001-6782-9188)
- Suresh Keshavamurthy (ORCID: https://orcid.org/0000-0002-5578-1911)
- Gaurav Sharma (ORCID: https://orcid.org/0000-0002-6951-2798)
- I. Timofte
- Srinivas Bollineni
- Michael E. Jessen (ORCID: https://orcid.org/0000-0001-5031-5279)
Institutions
- Southwestern Medical Center (US)
- Advanced Imaging Research (United States) (US)
- Association of Organ Procurement Organizations (US)
- All India Institute of Medical Sciences (IN)
- The University of Texas Southwestern Medical Center (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-11-25
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.45369
- Primary Topic
- Transplantation: Methods and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- American Heart Association