Digital Phenotyping and Cardiovascular Health

来自社交媒体、在线搜索和智能手机的数字数据可以揭示一个人的详细故事。 个人的日常活动。有关生活方式和健康行为的信息（如运动习惯、食物 消费、吸烟状况）往往通过这些电子平台披露大量细节。的 重要的是，许多共同的健康行为可能与心血管疾病有关， 治疗和管理。从这些电子媒介数据源导出的数字表型可以 塑造我们对人类疾病的评估，并具有超越我们传统方法的重大价值， 表征疾病表型（例如体格检查，实验室值），并最终扩展我们的能力， 识别和诊断健康状况并预测医疗保健利用率。这项建议的核心是 认识到人与人之间的沟通和在线活动，以前是私人的，现在是 可观察的正是这些新的沟通渠道的可观察性提供了创新和 对这一领域的调查作出承诺。我们的第一个目标是让病人同意分享他们的数字数据 (e.g.社交、搜索和移动的数据），并将该信息与经验证的健康记录数据合并在 研究数据库。然后，我们将广泛处理数字数据，使其成为可解释的格式， 可以结合到传统的预测模型中。目标2将侧重于评估以下方面的增量效益： 将数字数据添加到所述脆性风险评分以评估数字数据对预测的贡献， 心血管风险在未来，这些数据可以告知患者他们的个性化风险和方法， 切实改变这种风险。用于发布或共享数据的数字平台也可以直接用于 向患者提供关于他们使用的媒介的反馈，并直接响应他们所述的输入。 第三个目标将侧重于将数字数据纳入模型，以预测护理成本。这种方法提供 承诺更好地了解有助于医疗保健利用的因素，这些因素与 发病率、死亡率和心血管疾病的经济负担。通过这个项目，我们试图了解 关于收集和分析数字数据的新见解，同时关注道德和隐私问题 可能与这些数据有关。我们将在模型中加入数字数据来预测重要目标 比如冠心病风险和医疗保健使用。总的来说，这项赠款的重点领域代表了新的 精准医疗和心血管健康数字表型分析的前沿。

Digital data from social media, online searches, and smartphones can reveal a detailed narrative about an individual's day-to-day activities. Information about lifestyle and health behaviors (e.g. exercise habits, food consumption, smoking status) are often revealed with significant detail through these electronic platforms. Of importance, many commonly shared health behaviors may be associated with cardiovascular disease, treatment, and management. Digital phenotypes derived from these electronically mediated data sources can shape our assessment of human illness and have substantial value beyond our traditional approaches to characterizing a disease phenotype (e.g. physical exam, laboratory values), and ultimately expand our ability to identify and diagnose health conditions and predict healthcare utilization. Central to this proposal is the recognition that person-to-person communication and online activities that were previously private are now observable. It is the observability of these new communication channels that provides both innovation and promise to this area of inquiry. Our first aim will entail consenting patients to share access to their digital data (e.g. social, search, and mobile data) and merge this information with validated health record data in a research database. We will then extensively process the digital data so that it is in an interpretable format that can be incorporated in traditional predictive models. Aim 2 will focus on assessing the incremental benefit of adding digital data to the Framingham risk score to evaluate the contribution of digital data for predicting cardiovascular risk. In the future, this data could inform patients about their personalized risk and ways to concretely change that risk. The digital platforms used to post or share data could also be used to directly provide feedback to patients on the medium they use and in direct response to their stated inputs. The third aim will focus on incorporating digital data in models to predict cost of care. This approach offers promise for better understanding the factors contributing to healthcare utilization, which correlate with morbidity, mortality, and the economic burden of cardiovascular disease. Through this project we seek to learn new insights about collecting and analyzing digital data while being attentive to issues of ethics and privacy that may be associated with these data. We will incorporate digital data in models to predict important targets like coronary heart disease risk and healthcare use. Overall, the areas of focus for this grant represent new frontiers in precision medicine and digital phenotyping for cardiovascular health.