Deep phenotyping in Electronic Health Records for Genomic Medicine

摘要 可共享、创新和可扩展的方法来提取相关特征患者表型 电子健康记录(EHR)和系统地了解疾病关系对于 为患者实现疾病精准诊断和个性化疾病防治。 截至2020年5月28日，全球2019年新型冠状病毒(新冠肺炎)确诊病例5716271例， 包括美国的1,699,933例和200多个国家、地区和地区的356,124人死亡， 包括美国的100,442人死亡，这一数字还在不断攀升。大流行 对经济、社会和公共卫生产生了深远的影响。作为哥伦比亚大学欧文医学中心 (CUIMC)一直在纽约市震中前线抗击病毒，并治疗了超过 4100名SARS-CoV-2阳性患者，我们旨在通过以下方式解决新冠肺炎公共卫生的迫切需求 使用我们的电子病历数据开发可共享的表型分析方法来识别和表征新冠肺炎病例 和多种数据标准，包括观察性医疗结果伙伴关系(OMOP)共同 数据模型(CDM)和人类表型本体(HPO)，并生成关于COVID的新知识- 19例，如其危险因素、疾病亚型、临床期病程等。 我们对本附录的具体目标如下：对原始目标1的扩展：开发和验证 一种可扩展、可共享的新冠肺炎特征表型抽取方法 和非结构化的EHR数据，并使用以下方法标准化这些EHR表型的概念表示 广泛采用的数据标准，包括OMOP CDM、HPO、SNOMED-CT、UMLS和RxNorm。 对最初目标3的扩展：开发和验证新冠肺炎和 在异质人群中识别不同临床结局的疾病亚型的方法 新冠肺炎的深层特征ehr表型。 我们将向广大科学界和全国传播由此产生的方法和知识。 我们还将利用这一补充为博士后和研究生创造研究和培训机会 来自生物医学信息学、数据科学和计算机科学的学生，推进跨学科 数据科学和生物医学信息学方面的合作，以应对新冠肺炎和其他健康问题。

SUMMARY Sharable, innovative and scalable methods for abstracting relevant characteristic patient phenotypes from electronic health records (EHRs) and for systematically understanding disease relationships are critical for accomplishing precise disease diagnoses and personalized disease prevention and treatment for patients. As of May 28, 2020, there are 5,716,271 confirmed 2019 Novel Coronavirus (COVID-19) cases worldwide, including 1,699,933 cases in the United States, and 356,124 deaths across over 200 countries, areas, and territories including 100,442 deaths in the United States, with the numbers continually climbing. The pandemic has had profound economic, social, and public health impact. As Columbia University Irving Medical Center (CUIMC) has been fighting the virus on the frontline in the epicenter of New York City and treating more than 4,100 SARS-CoV-2 positive patients, we aim to address the urgent COVID-19 Public Heath need by developing sharable phenotyping methods to identify and characterize COVID-19 cases using our EHR data and multiple data standards, including the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and the Human Phenotype Ontology (HPO), and generate novel knowledge about COVID- 19, such as its risk factors, disease subtypes, and temporal clinical courses. Our specific aims for this supplement are as follows: Extension to the original Aim 1: Develop and validate scalable and sharable approaches to abstracting characteristic phenotypes of COVID-19 from both structured and unstructured EHR data and to standardize the concept representations of these EHR phenotypes using widely adopted data standards, including the OMOP CDM, HPO, SNOMED-CT, UMLS, and RxNorm. Extension to the original Aim 3: Develop and validate methods for temporal phenotyping for COVID-19 and methods for identifying disease subtypes of varying clinical outcomes among heterogeneous populations using deep characteristic EHR phenotypes of COVID-19. We will disseminate the resulting methods and knowledge with the broad scientific communities and the nation. We will also leverage this supplement to create research and training opportunities for postdocs and graduate students from biomedical informatics, data science and computer science, advancing interdisciplinary collaborations in data science and biomedical informatics to combat COVID-19 and other health problems.