Deep probabilistic predictive models for stroke and coronary heart disease

项目摘要 心血管疾病对全世界数百万人产生负面影响。在全球范围内， 占死亡人数的百分之三十此外，很大一部分由心血管疾病引起的死亡发生在 在非老年人群中;全世界所有死亡的15%归因于心血管疾病 为70岁以下的人服务。预防心血管事件的治疗应基于高度 个性化风险预测高风险患者应该得到更积极的治疗，因为 疾病超过治疗负担，而低风险患者应更保守地管理。 例如，冠心病的抗血栓治疗可能会增加出血风险， 适合低风险患者。两种主要的心血管疾病是中风和冠心病 这两种疾病的风险评分都有了一些进展。然而，这些风险 评分仅使用关于患者的可用测量的一小部分，并将风险视为 独立的因素，而不是考虑它们的相互作用如何放大或减轻风险。此外，多数 的流行的冠心病和中风风险评分被设计成由一个忙碌的忙碌 这进一步限制了它们的范围和保真度。下一代中风风险评分 和心血管疾病应该考虑到电子健康记录中的所有可用信息 而不受传统风险建模的参数假设的约束。更准确的风险评估 减少冠心病和中风的发病率将带来更好的护理，减少心血管疾病的负担。 我们的愿景是利用大量电子健康记录，沿着最新进展， 通过深度学习来建立风险评分，使用更多可用的健康信息，同时将数学计算减少到最低限度。 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 将以前技术限制所必需的计算风险转移到利用深度学习的计算 学习高度非线性的风险和风险因素的相互作用。我们还展示了深度学习如何 用来处理医学中一直存在的缺失值问题。我们的建议亦针对以下范畴： 通过以前的风险评分工作探索：公平性。治疗质量受风险估计质量的影响。 这意味着估计风险不太准确的人群可能会得到更差的护理。制定风险评分 由于简单模型不灵活， 足以覆盖多个人群。我们试图确定以下方面的潜在风险计算差异 种族和民族。我们将构建和评估用于冠心病和中风的深度学习方法 电子健康记录的风险评估。我们将开发技术，将临床文本，处理 缺失数据，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 在临床会议和出版物上以深度学习框架编写的开源代码的形式提供。

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.