Prediction of Health Outcomes and Adverse Events in Pediatric Organ Transplantation in Florida

佛罗里达州儿科器官移植的健康结果和不良事件预测

基本信息

  • 批准号:
    10353583
  • 负责人:
  • 金额:
    $ 18.91万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-02-10 至 2024-01-31
  • 项目状态:
    已结题

项目摘要

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.
摘要 先前的研究提供了初步证据,表明机器学习算法可以用于预测 儿科器官移植的术后结果。目前该领域的预测模型提供了 不能令人满意的预测准确性,并没有检查患者和家族风险的纵向影响 影响移植后结果的因素。R21探索性/发展性研究补助金的目的 项目建议是将高级预测模型的使用整合到儿科疾病的预测中。 移植后的健康结果,以提高对患者和移植物存活率的预测。在一次合作中 在佛罗里达州立大学(FSU)、佛罗里达大学(UF)和迈阿密大学(UM)之间, 拟议的R21项目将支持通过先进的技术预测移植后健康结果的研究。 儿科器官移植患者的预测模型。该项目的总体目标和新奇是, 使用患者电子健康记录(EHR)数据,中心特定的器官共享联合网络(UNOS) 数据,以及来自佛罗里达两个最大的移植中心的文本临床数据与机器学习(ML), 深度学习(DL)和自然语言处理(NLP)来开发多个预测模型, 儿童移植后的结果。我们建议分析多个数据集以更好地了解风险因素 影响儿童移植后结局的因素,包括人口统计学、家庭、医疗、健康和其他 移植后特征移植后结果包括晚期急性排斥反应,需要再次移植, and mortality.我们的中心假设是,长期移植后的结果将更有效地 通过使用先进的ML,DL,和 NLP分析方法我们的长期目标是提高儿科移植团队预测 新出现的移植后不良结局,识别高风险患者,减少健康差距,并促进 这些患者的健康状况和生活质量。结果将为临床决策的制定提供信息- 为移植医生和团队制作工具,允许更有效和及时的识别, 对移植后不良结局风险最高的儿童和家庭进行适当干预。

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