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

癌症预防、诊断和治疗的进步极大地提高了癌症的长期存活率 那些被诊断为乳腺癌的人。然而，这一成功已被平行增加的 乳腺癌幸存者的慢性病发病率，特别是心血管疾病 至少部分归因于心脏毒性治疗方案。当前以证据为基础的预防和预防指南 控制乳腺癌幸存者的心血管疾病是广泛的，我们缺乏明确的评估指南。 心血管事件的个体化风险。现有的心血管疾病风险预测模型主要集中在一般风险预测上。 并且只依赖于有限数量的变量。电子产品的采用和整合 健康记录(EHR)系统提供了关于个人特征的丰富信息 护理要点，包括非结构化的临床叙述、成像数据和结构化的临床变量。 然而，现实世界的EHR数据是高度不平衡的，包括心血管疾病患者的比例 BC诊断以来CVD发生的转归和时间的均匀分布。我们的 首要目标是为学习建立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一个计算框架 使用多模式EHR数据预测BC幸存者的心血管疾病风险。成功地实施了 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 预测不列颠哥伦比亚州心血管疾病预后的工具。

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.