COVID-19 disease course analysis using multi-site large-scale EHR data

项目摘要/摘要 自2019年12月报告首例fi以来，冠状病毒病-2019年(新冠肺炎)已造成一场泛滥。 艾滋病在188个国家/地区蔓延，并引发了前所未有的健康、经济和社会危机。在……里面 为了应对大流行的动荡动态和严重性，我们必须确定 新冠肺炎感染的各种临床病程，并决定是否以及如何人口学、临床等 fl中的变量会影响它们。关于疾病传播、症状、临床病程、治疗的知识 基于许多来源，结果正在迅速演变。促进这一知识的重要来源 是来自电子健康记录(EHR)和健康信息交换(HIE)的数据，因为它们可以帮助 实时、不加掩饰地查看疾病。使用大规模、集成良好和丰富的EHR数据实现 对新冠肺炎病程的全面预测和量化，可直接指导临床实践。 蒂斯。我们研究的长期目标是开发Artifi社交智能(AI)工具，以促进访问和 临床资料分析。这个应用程序的目标是开发有效的算法和工具来挖掘临床 对新冠肺炎的病程进行分类，并确定临床和其他变量的影响。 与他们联系在一起。我们将使用来自大量全面的健康信息的数据来开发我们的算法 印第安纳州患者护理网络，拥有约40,000名新冠肺炎患者和公平的 完成有关他们的电子病历数据。我们将针对包括EHR数据在内的其他数据集对算法进行评估 来自俄亥俄州立大学韦克斯纳医学中心和国家COVID队列合作(N3C)。fic目标的规范 本项目的主要目的是(1)建立新冠肺炎病程分组；(2)与其他临床合并症相关 变量对新冠肺炎病程的影响，以及(3)在N3C数据上验证所开发的算法。这位亲王- Posal是重要的fi不能，因为在此项目中开发的方法有可能显著增加我们的fi 能够对大流行期间及以后的大量和丰富的患者数据进行计算分析； 优势来自我们针对庞大、包容的患者群体提供的全面fi新冠肺炎课程 在丰富的电子病历数据的支持下，可以对临床实践产生积极的影响；本项目开发的工具将是 作为免费新冠肺炎研究资源向公众发布。这是创新的，因为我们的方法整合了 新的方法，如使用临床变量和疾病进展轨迹的患者聚类，以及过程。 倾斜轨迹比较，与建立的单变量和预测分析；我们的主要方法将杠杆- 美国历史最悠久、规模最大的高等教育机构之一，以获取有关 庞大的患者群体；以及本项目产生的强大的初步数据有助于改善新冠肺炎 患者表型、疾病特征和诊断。

Project Summary/Abstract Since its first case reported in December 2019, the coronavirus disease-2019 (COVID-19) has caused a pan- demic in 188 countries/regions, and has precipitated an unprecedented health, economic and social crisis. In order to cope with the volatile dynamic and severity of the pandemic, it is imperative that we characterize the various clinical courses of COVID-19 infection, and determine whether and how demographic, clinical and other variables influence them. Knowledge of the disease's transmission, symptomatology, clinical course, treatment and outcomes is rapidly evolving based on many sources. An important source for advancing this knowledge is data from electronic health records (EHR) and health information exchanges (HIE) because they can pro- vide a real-time, unvarnished view of the disease. Using large-scale, well-integrated and rich EHR data enables comprehensive profiling and quantification of the COVID-19 disease course that can directly inform clinical prac- tice. The long-term goal of our research is to develop Artificial Intelligence (AI) tools to facilitate access to and analysis of clinical data. The goal of this application is to develop effective algorithms and tools to mine clinical data to categorize disease courses of COVID-19, and determine the effect of clinical and other variables asso- ciated with them. We will develop our algorithms using data from a large and comprehensive health information exchange, the Indiana Network for Patient Care (INPC), which has about 40,000 COVID-19 patients and fairly complete EHR data about them. We will evaluate the algorithms against other data sets, including EHR data from the OSU Wexner Medical Center and the National COVID Cohort Collaborative (N3C). The specific aims of this project are to (1) develop COVID-19 disease course groupings, (2) relate comorbidities and other clinical variables to the COVID-19 disease course, and (3) validate the developed algorithms on N3C data. This pro- posal is significant because the methods developed in this project have the potential to significantly increase our capability for computational analysis of large and rich patient data during the pandemic and beyond; the knowl- edge derived from our comprehensive profiling of COVID-19 courses over large, inclusive patient populations supported by rich EHR data can positively impact clinical practice; and the tools developed in this project will be released to the public as a free COVID-19 research re- source. It is innovative because our methods integrate novel methods such as patient clustering using clinical variables and disease progression trajectories, and pa- tient trajectory comparison, with established univariate and predictive analysis; our primary approach will lever- age the oldest and one of the country's largest HIEs to derive detailed and comprehensive knowledge about a large patient population; and the strong preliminary data generated by this project can help improve COVID-19 patient phenotyping, disease characterization and diagnosis.