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展极大地改善了癌症患者的长期生存率。 那些被诊断患有乳腺癌的人。然而，这一成功受到了平行增长的影响， 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。目前的循证指南， 控制乳腺癌幸存者的心血管疾病是广泛的，我们缺乏明确的指导， 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的 人口，只依赖于有限数量的变量。采用和整合电子 健康记录（EHR）系统提供了大量关于个人特征的信息， 床旁，包括非结构化临床叙述、成像数据和结构化临床变量。 然而，真实世界的EHR数据是高度不平衡的，包括CVD患者的比例 结果和自BC诊断以来CVD发展时间的均匀分布。我们 总体目标是发展坚实的计算和理论基础， 不平衡的真实世界数据，重点是BC-CVD结果风险预测。具体来说，我们将 为不平衡分类和不平衡回归任务开发计算框架， 使用多模式EHR数据预测BC幸存者的CVD风险。成功实施 该项目将为不平衡学习奠定计算基础，并可以提供更准确的 预测BC CVD结果的工具。