SCH: A New Computational Framework for Learning from Imbalanced Biomedical Data

SCH：一种从不平衡生物医学数据中学习的新计算框架

• 批准号: 10816630
• 负责人: Ying Cui
• 金额: $ 30万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10816630
• 关键词: Adoption; Breast Cancer survivor; Cardiotoxicity; Cardiovascular Diseases; Characteristics; Chronic; Classification; Clinical; Data; Development; Diagnosis; Disease Outcome; Electronic Health Record; Event; Foundations; General Population; Goals; Image; Incidence; Individual; Learning; Morbidity - disease rate; Patients; Solid; Structure; Survival Rate; Time; Treatment Protocols; breast cancer diagnosis; cancer prevention; cardiovascular disorder risk; cardiovascular risk factor; computer framework; electronic health record system; evidence based guidelines; improved; malignant breast neoplasm; mortality; multimodality; point of care; predictive tools; prevent; risk prediction; risk prediction model; success

Advances in cancer prevention, diagnosis, and treatment have dramatically improved long-term survival of those diagnosed with breast cancer. However, this success has been tempered by a parallel increased incidence of chronic conditions in breast cancer survivors, in particular cardiovascular disease (CVD), due at least in part to cardiotoxic treatment regimens. Current evidence-based guidelines for preventing and controlling CVD in breast cancer survivors are broad, and we lack clear guidance for assessing individualized risks of cardiovascular events. Existing CVD risk prediction models focus on the general population and rely only on a limited number of variables. The adoption and integration of electronic health record (EHR) systems has provided a wealth of information about individual characteristics at the point of care, including unstructured clinical narratives, imaging data, and structured clinical variables. However, the real-world EHR data is highly imbalanced including the fraction of patients with CVD outcomes and the uniform distribution of time for the CVD development since BC diagnosis. Our overarching goal is to develop solid computational and theoretical foundations for learning from imbalanced real-world data, with an emphasis on BC-CVD outcome risk prediction. Specifically, we will develop a computational framework for imbalanced classification and imbalanced regression tasks on the CVD risk prediction among BC survivors using multimodal EHR data. The successful implementation of this project would lay a computational foundation for imbalanced learning and can provide more accurate tools for predicting BC CVD outcomes.

癌症预防、诊断和治疗方面的进步极大地提高了癌症患者的长期生存率 被诊断患有乳腺癌的人。然而，这一成功却因平行增长而受到影响。 乳腺癌幸存者中慢性疾病的发病率，特别是心血管疾病（CVD）， 至少部分与心脏毒性治疗方案有关。目前基于证据的预防和预防指南 控制乳腺癌幸存者的心血管疾病的范围很广泛，我们缺乏明确的评估指导 心血管事件的个体化风险。现有的CVD风险预测模型侧重于一般情况 人口，仅依赖于有限数量的变量。电子技术的采用和集成 健康记录（EHR）系统提供了有关个人特征的丰富信息 护理点，包括非结构化临床叙述、影像数据和结构化临床变量。 然而，现实世界的 EHR 数据高度不平衡，包括 CVD 患者的比例 自 BC 诊断以来 CVD 发展的结果和时间的均匀分布。我们的 总体目标是为学习奠定坚实的计算和理论基础 不平衡的现实世界数据，重点是 BC-CVD 结果风险预测。具体来说，我们将 开发一个用于不平衡分类和不平衡回归任务的计算框架 使用多模式 EHR 数据预测 BC 幸存者的 CVD 风险。的成功实施 该项目将为不平衡学习奠定计算基础，并且可以提供更准确的 预测 BC CVD 结果的工具。