Deep phenotyping in Electronic Health Records for Genomic Medicine

总结 可共享的、创新的和可扩展的方法，用于从 电子健康记录（EHR）和系统地了解疾病关系是至关重要的， 实现对患者的精准疾病诊断和个性化疾病防治。 截至2020年5月28日，全球共有5，716，271例2019新型冠状病毒（COVID-19）确诊病例， 包括美国的1，699，933例，200多个国家，地区和 包括美国在内的100，442人死亡，数字不断攀升。疫情 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）一直在纽约市震中的前线抗击病毒， 4，100名SARS-CoV-2阳性患者，我们的目标是通过以下方式解决紧急的COVID-19公共卫生需求： 利用我们的EHR数据开发可共享的表型分析方法，以识别和表征COVID-19病例 和多个数据标准，包括观察性医学成果伙伴关系（OMOP）共同 数据模型（CDM）和人类表型本体（HPO），并生成有关COVID的新知识- 19，如其危险因素，疾病亚型，和时间的临床过程。 我们的具体目标如下：扩展到原来的目标1：开发和验证 从结构化和可扩展的方法中提取COVID-19的特征表型 和非结构化的EHR数据，并使用 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COVID-19的时间表型分析方法， 用于鉴定异质群体中不同临床结果的疾病亚型的方法， COVID-19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，创造研究和培训的机会，博士后和研究生 学生从生物医学信息学，数据科学和计算机科学，推进跨学科 在数据科学和生物医学信息学方面的合作，以应对COVID-19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.