Efficient Statistical Learning Methods for Personalized Medicine Using Large Scale Biomedical Data

项目总结： 冠状病毒病19(新冠肺炎)在全球范围内造成重大公共卫生危机。这部小说 观察到冠状病毒潜伏期长，在此期间传染性极强。没有经过验证的 有有效的治疗方法或疫苗可用。在许多国家实施了大规模的公共干预 以及美国(US)处于疫情不同阶段的州，具有不同的社会分布组合 跳跃、流动限制与种群行为变化。关于如何实施这些干预措施的决定 (例如，何时施加和放松缓解措施)依赖于COVID流行病学的重要统计数据(例如， 有效复制数)来刻画和预测新冠肺炎的爆发过程。然而，还有 缺乏能准确反映冠状病毒流行异质性的健壮而简约的模型 易感人群和地区(例如，人口统计、医疗保健能力、社会和经济决定因素)。 没有严格的研究来指导针对人群或地区量身定做的精确公共卫生干预措施 这取决于它们的特征。此外，由于公共卫生干预的非随机性， 在比较各国新冠肺炎缓解措施时，考虑到偏差和混淆至关重要。 吉昂斯。为了应对这些挑战，该项目开发了稳健和可推广的分析方法来评估 公共卫生干预措施，并评估个别患者感染新冠肺炎和并发症的风险。在目标1中， 我们将开发动态和稳健的统计模型来预测疾病流行。这些模型将估计 fi首次不明感染病例的发生日期、瞬时有效繁殖数，以及引起感染的原因。 新冠肺炎病毒的潜伏期。此外，人口结构、社会和经济的异质性 将纳入指标、卫生保健能力和地理位置，以评估它们对冠状病毒感染的影响 流行病。在纵向准实验设计下，我们将为比较公众提供有效的推断 在不同地区实施的卫生干预措施，同时考虑到混淆的偏见。多个来源 来自美国不同州的数据将被分析，以经验地测试哪些州的应对策略更多 有效和在哪个亚群中。在目标2中，我们将专注于开发精确的个人风险评估工具-- 使用纽约长老会医院收集的电子健康记录(EHR)的新冠肺炎UAL患者 纽约，新冠肺炎的中心。我们将设计与严重疾病相关的患者前置条件的特征 COVID并发症、康复或死亡。更重要的是，我们将设计代表病毒代理的功能 患者地理信息的暴露。我们将使用机器学习技术来创建量化 患者预后摘要(例如，过渡到严重的临床阶段、出院、死亡)。我们将使用内部- NAL交叉验证和外部校准以验证所开发的算法。该项目将产生证据 指导精准的公共卫生干预，优化患者护理，并有效地分配fi医疗资源 预计COVID疫情将出现第二波，并为其他传染病暴发做好准备。

Project Summary: Coronavirus disease 19 (COVID-19) has created a major public health crisis around the world. The novel coronavirus was observed to have a long incubation period and extremely infectious during this period. No proven effective treatment or vaccine is available. Massive public interventions have been implemented in many countries and states in the United States (US) at different phases of the outbreak with varying combinations of social dis- tancing, mobility restriction and population behavioral change. Decisions on how to implement these interventions (e.g., when to impose and relax mitigation measures) rely on important statistics of COVID epidemiology (e.g., effective reproduction number) that characterize and predict the course of COVID-19 outbreak. However, there is a lack of robust and parsimonious model of COVID epidemic that can accurately reflect the heterogeneity between susceptible populations and regions (e.g., demographics, healthcare capacity, social and economic determinants). There is no rigorous study to guide precision public health interventions that are tailored to a population or region depending on their characteristics. Furthermore, due to the non-randomized nature of public health interventions, it is critical to account for biases and confounding when comparing mitigation measures of COVID-19 across re- gions. To address these challenges, this project develops robust and generalizable analytic methods to evaluate public health interventions and assess individual patient risks of COVID-19 infection and complications. In Aim 1, we will develop dynamic and robust statistical models to predict the disease epidemic. The models will estimate the date of the first unknown infection case, instantaneous effective reproduction number, and account for the incu- bation period of COVID-19 virus. Furthermore, heterogeneity in population's demographics, social and economic indicators, healthcare capacity and geographic locations will be incorporated to reflect their impacts on COVID epidemic. Under a longitudinal quasi-experimental design, we will provide valid inference for comparing public health interventions implemented at different regions while accounting for confounding bias. Multiple sources of data from different states in the US will be analyzed to empirically test which states' response strategies are more effective and in which subpopulation. In Aim 2, we will focus on developing precise risk assessment tool of individ- ual COVID-19 patients using electronic health records (EHRs) collected at New York Presbyterian hospital in New York City, an epicenter of COVID-19. We will engineer features of patient's pre-conditions associated with severe COVID complications, recovery, or death. More importantly, we will engineer features that represent proxies of virus exposures from patients' geographic information. We will use machine learning techniques to create quantitative summaries of patient prognosis (e.g., transitioning to serious clinical stages, discharge, death). We will use inter- nal cross-validation and external calibration to validate developed algorithms. The project will generate evidence to guide precision public health intervention, optimal patient care, and efficient healthcare resource allocation in anticipation of a second wave of COVID epidemic and in preparation of other infectious disease outbreaks.