PSECP and PCOP invite the authors to discuss machine learning (ML) models predicting post-transplant hospitalization, and the broader aspects of artificial intelligence in vulnerable transplant populations.
"Machine learning–based prediction of health outcomes in pediatric organ transplantation recipients"
(JAMIA Open. 2021 Mar 12;4(1):ooab008. doi: 10.1093/jamiaopen/ooab008. eCollection 2021 Jan.)
In this article:
Prediction of post-transplant health outcomes and identification of key factors remain important issues for pediatric transplant teams and researchers... The purpose of [this] study was to examine machine learning (ML) models predicting post-transplant hospitalization in a sample of pediatric kidney, liver, and heart transplant recipients from a large solid organ transplant program.
The panelists also plan to touch on broader aspects of artificial intelligence in vulnerable transplant populations as part of the discussion.
Speaker:
- Michael Killian, PhD, MSW • Florida State University College of Social Work, Tallahassee, FL
Moderator:
- Ashley Spann, MD, MS Applied Clinical Informatics • Vanderbilt University Medical Center, Nashville, TN
Co-sponsored by AST's Psychosocial and Ethics Community of Practice (PSECOP) and Pediatrics Community of Practice (PCOP). All AST Journal Clubs are free but registration is required to attend live.