Deep probabilistic predictive models for stroke and coronary heart disease

项目摘要 心血管疾病对全球数百万人造成负面影响。在全球范围内，它约占 占所有死亡人数的30%。此外，心血管疾病造成的死亡中有很大一部分发生在 在非老年人群中，全世界15%的死亡归因于心血管疾病 为70岁以下的人准备的。预防心血管事件的治疗应高度重视 个性化风险预测。高危患者应该接受更积极的治疗，因为 疾病超过了治疗的负担，而低风险患者应该得到更保守的管理。 例如，冠心病的抗血栓治疗可能会增加出血风险，但可能不会 适用于低风险患者。两种主要的心血管疾病是中风和冠心病 这两种疾病的风险分数都有了一些变化。然而，这些风险 Score只使用有关患者的可用测量的一小部分，并将风险视为 独立因素，而不是考虑它们之间的相互作用如何放大或改善风险。此外，大多数人 流行的冠心病和中风风险评分是由忙碌的人手动计算的 医生在看护点，这进一步限制了他们的范围和保真度。中风的下一代风险评分 心血管疾病应该考虑到电子健康记录中的所有可用信息 不受传统风险建模参数假设的约束。更准确的风险评估 预防冠心病和中风将带来更好的护理和减轻心血管疾病的负担。 我们的愿景是利用大量的电子健康记录以及最近在 深度学习，以构建使用更多可用健康信息的风险分值，同时最大限度地减少数学运算 关于临床风险性质的假设。我们的建议推动了该领域从人类可计算的独立 由于以前技术对利用深度学习的计算的限制而必须进行的风险计算 学习高度非线性的风险和风险因素的相互作用。此外，我们还展示了深度学习可以是 用来处理医学中一直存在的价值缺失的问题。我们的建议亦针对下列地区- 由先前关于风险分数的工作探索：公平性。风险评估的质量直接影响治疗质量。 这意味着风险估计不太准确的人群可能会得到更差的护理。制定风险分值 使用简单模型可能只能准确地捕捉大多数人群的风险，因为简单模型不灵活 足以覆盖多个人口。我们试图确定以下方面的潜在风险计算差异 种族和民族。我们将构建和评估针对冠心病和中风的深度学习方法 来自电子健康记录的风险评估。我们将开发技术来整合临床文本、句柄 丢失数据，并评估深度学习对心血管风险评分的公平性。最后，我们将使我们的工作 在深度学习框架、临床会议和出版物中以开放源代码的形式提供。

Project Summary Cardiovascular disease negatively affects millions of people worldwide. Globally, it accounts for approximately thirty percent of all deaths. Furthermore, a significant fraction of deaths caused by cardiovascular disease occur in a non-geriatric population; fifteen percent of all worldwide deaths are attributed to cardiovascular disease for people under the age of seventy. Treatment to prevent cardiovascular events should be based on highly individualized risk prediction. High risk patients should get more aggressive treatments because the risk of disease outweighs the burden of treatment, while low risk patients should be managed more conservatively. For example, anti-thrombotic therapy for coronary heart disease may increase bleeding risk and may not be appropriate for low-risk patients. Two primary kinds of cardiovascular disease are stroke and coronary heart disease, and there have been a number of developments in risk scores for both ailments. However, these risk scores only use a small fraction of the available measurements about a patient and treat risk as a collection of independent factors rather than considering how their interactions amplify or ameliorate risk. Moreover, a majority of the popular coronary heart disease and stroke risk scores are designed to be manually computed by a busy physician at the point of care, which further limits their scope and fidelity. Next generation risk scores for stroke and cardiovascular disease should take into account all of the available information in the electronic health record without the constraints of the parametric assumptions of traditional risk modeling. More accurate risk assessment of coronary heart disease and stroke will lead to better care and reduce the cardiovascular disease burden. Our vision is to capitalize on large collections of electronic health records along with recent advances in deep learning to build risk scores that use more available health information while making minimal mathematical assumptions about the nature of clinical risk. Our proposal propels the field from human computable independent risks calculations necessitated by previous limitations of technology to calculations that make use of deep learning to learn highly nonlinear risks and risk factor interactions. We additionally demonstrate how deep learning can be used to deal with the ever-present issue of missing values in medicine. Our proposal also targets an area under- explored by previous work on risk scores: fairness. Treatment quality is affected by the quality of risk estimation. This means populations where estimated risk is less accurate may receive worse care. Risk scores developed with simple models may only capture risk accurately for the majority population as simple models are not flexible enough to cover multiple populations. We seek to identify potential risk calculation differences with respect to race and ethnicity. We will construct and evaluate deep learning methods for coronary heart disease and stroke risk assessment from electronic health records. We will develop techniques to incorporate clinical text, handle missing data, and evaluate fairness of deep learning for cardiovascular risk scores. Finally, we will make our work available as open source code written in deep learning frameworks, at clinical conferences, and publications.