Deep phenotyping in Electronic Health Records for Genomic Medicine

基因组医学电子健康记录中的深度表型分析

基本信息

批准号：
10175742
负责人：
CHUNHUA WENG
金额：
$ 7.5万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2021-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10175742
关键词：
2019-nCoV Address Administrative Supplement Adopted Algorithms Area COVID-19 Cessation of life Characteristics Clinical Clinical Course of Disease Clinical Data Communities Country Data Data Science Data Set Disease Economics Electronic Health Record Genomic medicine Goals Grant Health Human Knowledge Knowledge Discovery Medical Medical center Methods Mining New York City Ontology Outcome Parents Patients Phenotype Population Heterogeneity Postdoctoral Fellow Public Health Research Research Training Risk Factors SNOMED Clinical Terms Standardization Structure System Terminology Unified Medical Language System United States Universities Virus abstracting biomedical informatics combat computer science data modeling data standards disease diagnosis disorder prevention disorder subtype fighting graduate student health data infection risk innovation interdisciplinary collaboration interest novel pandemic disease parent grant phenotypic data social training opportunity

项目摘要

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.

概括可共享、创新和可扩展的方法，用于从患者中提取相关的特征性表型电子健康记录 (EHR) 和系统地了解疾病关系对于为患者实现精准的疾病诊断和个性化的疾病防治。截至 2020 年 5 月 28 日，全球已确诊 5,716,271 例 2019 新型冠状病毒（COVID-19）病例，其中美国有1,699,933例确诊病例，200多个国家、地区和地区有356,124人死亡其中美国有 100,442 人死亡，而且这一数字还在不断攀升。大流行产生了深远的经济、社会和公共卫生影响。作为哥伦比亚大学欧文医学中心（CUIMC）一直在纽约市中心的前线抗击病毒，并治疗了超过 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：使用我们的 EHR 数据开发可共享的表型分析方法来识别和表征 COVID-19 病例和多种数据标准，包括观察性医疗结果合作伙伴关系 (OMOP) 通用标准数据模型（CDM）和人类表型本体（HPO），并生成有关新冠病毒的新知识- 19，例如其危险因素、疾病亚型和时间临床过程。我们对此补充的具体目标如下：扩展最初的目标 1：开发和验证可扩展且可共享的方法从结构化数据中提取 COVID-19 的特征表型和非结构化 EHR 数据，并使用这些 EHR 表型的概念表示标准化广泛采用的数据标准，包括 OMOP CDM、HPO、SNOMED-CT、UMLS 和 RxNorm。最初目标 3 的延伸：开发和验证 COVID-19 的时间表型分析方法识别异质人群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。我们还将利用此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，推进跨学科发展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。

项目成果

期刊论文数量（17）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Clusters, crop dusters, and myth busters: a scoping review of environmental exposures and cutaneous T-cell lymphoma.

集群、农作物喷粉机和神话终结者：环境暴露和皮肤 T 细胞淋巴瘤的范围审查。

DOI：
10.23736/s2784-8671.23.07729-0
发表时间：
2023
期刊：
Italian journal of dermatology and venereology
影响因子：
1.9
作者：
Gordon,EmilyR;Adeuyan,Oluwaseyi;Schreidah,CelineM;Chen,Caroline;Trager,MeganH;Lapolla,BrigitA;Fahmy,LaurenM;Weng,Chunhua;Geskin,LarisaJ
通讯作者：
Geskin,LarisaJ

Multi-label topic classification for COVID-19 literature with Bioformer.

使用 Bioformer 对 COVID-19 文献进行多标签主题分类。

DOI：
发表时间：
2022
期刊：
ArXiv
影响因子：
0
作者：
Fang,Li;Wang,Kai
通讯作者：
Wang,Kai

Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT.

DOI：
10.1016/j.patter.2023.100887
发表时间：
2024-01-12
期刊：
PATTERNS
影响因子：
6.5
作者：
Yang, Jingye;Liu, Cong;Deng, Wendy;Wu, Da;Weng, Chunhua;Zhou, Yunyun;Wang, Kai
通讯作者：
Wang, Kai

Pathway analysis of genomic pathology tests for prognostic cancer subtyping.

用于预后癌症亚型的基因组病理学测试的路径分析。

DOI：
10.1016/j.jbi.2019.103286
发表时间：
2019
期刊：
Journal of biomedical informatics
影响因子：
4.5
作者：
Lyudovyk,Olga;Shen,Yufeng;Tatonetti,NicholasP;Hsiao,SusanJ;Mansukhani,MaheshM;Weng,Chunhua
通讯作者：
Weng,Chunhua

Comparative effectiveness of medical concept embedding for feature engineering in phenotyping.