Abstract Prior research has provided initial evidence that machine learning algorithms can be used to predict posttransplant outcomes in pediatric organ transplantation. Current prediction modeling in this area offers unsatisfactory predictive accuracy and has not examined the longitudinal effects of patient and familial risk factors on posttransplant outcomes. The objective of this R21 Exploratory/Developmental Research Grant Program proposal is to integrate the use of advanced predictive modeling into the prediction of pediatric posttransplant health outcomes in order to improve prediction of patient and graft survival. In a collaboration between Florida State University (FSU), the University of Florida (UF), and the University of Miami (UM), the proposed R21 project will support research predicting posttransplant health outcomes through advanced predictive modeling in pediatric organ transplant patients. The overall objective and novelty of this project is to use patient electronic health record (EHR) data, center-specific United Network for Organ Sharing (UNOS) data, and textual clinical data from the two largest transplant centers in Florida with machine learning (ML), deep learning (DL), and natural language processing (NLP) to develop multiple predictive models of posttransplant outcomes in children. We propose to analyze multiple datasets to better understand risk factors that affect posttransplant outcomes in children, including demographic, familial, medical, health, and other posttransplant characteristics. Posttransplant outcomes include late acute rejection, need for retransplantation, and mortality. Our central hypothesis is that long-term posttransplant outcomes will be more effectively predicted by a combination of psychosocial and medical risk factors through the use of advanced ML, DL, and NLP analytic approaches. Our long-term goal is to improve the ability of pediatric transplant teams to predict emerging poor posttransplant outcomes, identify high-risk patients, reduce health disparities, and promote health outcomes and quality of life in these patients. Results will inform the development of a clinical decision- making tool for transplant physicians and teams, allowing more efficient and timely identification and appropriate interventions with children and families at most risk for poor posttransplant outcomes.

Prediction of Health Outcomes and Adverse Events in Pediatric Organ Transplantation in Florida Project Narrative / Public Health Relevance Prior research has provided initial evidence that machine learning algorithms can be used to predict posttransplant outcomes and survival in pediatric organ transplantation including late acute rejection, increased number and frequency of hospitalizations, need for retransplantation, and even mortality. The objective of this R21 Exploratory Collaborative Innovation proposal is to integrate the use of advanced predictive modeling into the prediction of pediatric posttransplant health outcomes in order to improve prediction of patient and graft survival. Results will inform the development of a decision-making tool for transplant physicians and teams, allowing more efficient and timely identification and appropriate interventions with children and families at most risk for poor posttransplant outcomes.