Talks and presentations

Learning from Simulated Patient Trajectories: A Sequential Decision-Making Agent for Heart Transplantation

October 26, 2025

Invited Talk, INFORMS Annual Meeting 2025, Atlanta, GA, USA

Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but the process remains largely ad-hoc. With the growing volume of longitudinal patient, donor, and organ data collected by the United Network for Organ Sharing (UNOS) since 2018, there is increasing interest in analytical approaches to support clinical decision-making at the time of organ availability.

Benchmarking Waitlist Mortality Prediction Through Time-to-Event Modeling using New UNOS Dataset

August 15, 2025

Oral and Poster Presentation, Machine Learning for Healthcare 2025, Rochester, MN, USA

On Interpretable Heart Transplant Mortality Prediction and Benchmarking

August 03, 2025

Oral Presentation, World Transplant Congress 2025, San Francisco, CA, USA

Interpretable Mortality Simulation and Decision-Making Agent for Heart Transplantation

October 21, 2024

Invited Talk, INFORMS Annual Meeting 2024, Seattle, WA, USA

Decisions regarding acceptance of a donor’s heart for a patient on the transplant waitlist is currently based on evaluation by a committee of doctors who review many factors but are still quite ad-hoc. Given the volume and range of data collected by the United Network for Organ Sharing (UNOS) on waitlisted patients, donors, clinicians and organs, there is increasing interest among all stakeholders, particularly clinicians, in leveraging model-based approaches that learn from the vast amount of historical data on organ acceptance/rejection to optimize the heart transplantation outcomes and assist human decision making. In response to this need, we benchmark and propose machine learning models to provide accurate and interpretable predictions regarding transplant mortality at both the individual and population levels. The new level of accuracy provides a reliable foundation upon which clinicians can base their decisions with a greater degree of confidence. We have also identified the key features that drive the predictions and verified the clinical significance of these results through close collaboration with the domain experts.