National Infrastructure for Standardized and Portable EHR Phenotyping Algorithms

标准化和便携式 EHR 表型算法的国家基础设施

• 批准号: 10021669
• 负责人: YUAN LUO
• 金额: $ 70.69万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10021669
• 关键词: Address; Adoption; Affect; Algorithms; Architecture; Benchmarking; Benign Prostatic Hypertrophy; Clinical; Clinical Data; Clinical Research; Clinical Trials; Code; Collaborations; Communities; Complex; Computerized Medical Record; Computing Methodologies; Consensus; Consumption; Data; Data Element; Data Reporting; Development; Educational workshop; Electronic Health Record; Electronic Medical Records and Genomics Network; Engineering; Event; Exclusion Criteria; Fast Healthcare Interoperability Resources; Flowcharts; Genomics; Gold; Grant; Health; Health system; Healthcare Systems; Human; Informatics; Infrastructure; Intuition; Knowledge; Logic; Measures; Medical; Methods; Modeling; Natural Language Processing; Needs Assessment; Observational Study; Outcome; Patients; Performance; Phase; Phenotype; Precision Medicine Initiative; Process; Public Health; Public Health Informatics; Rare Diseases; Research; Research Personnel; Resolution; Resources; Risk Factors; Running; Scientist; Services; Standardization; Structure; System; Techniques; Text; Time; Translational Research; United States National Institutes of Health; University Hospitals; authority; base; biobank; clinical phenotype; cohort; comparative effectiveness study; computable phenotypes; cost; data modeling; data warehouse; database query; deep learning; design; endophenotype; experience; inclusion criteria; informatics training; information model; knowledge base; meetings; phenotyping algorithm; portability; precision medicine; repository; structured data; syntax; tool; usability

PROJECT SUMMARY With the rapidly growing adoption of patient electronic health record systems (EHRs) due to Meaningful Use, and linkage of EHRs to research biorepositories, evaluating the suitability of EHR data for clinical and translational research is becoming ever more important, with ramifications for genomic and observational research, clinical trials, and comparative effectiveness studies. A key component for identifying patient cohorts in the EHR is to define inclusion and exclusion criteria that algorithmically select sets of patients based on stored clinical data. This process is commonly referred to, as “EHR-driven phenotyping” is time-consuming and tedious due to the lack of a widely accepted and standards-based formal information model for defining phenotyping algorithms. To address this overall challenge, the proposed project will design, build and promote an open-access community infrastructure for standards-based development and sharing of phenotyping algorithms, as well as provide tools and resources for investigators, researchers and their informatics support staff to implement and execute the algorithms on native EHR data.

项目总结 随着由于有意义的使用而迅速采用患者电子健康记录系统(EHR)， 以及将电子病历与研究生物信息库联系起来，评估电子病历数据对临床和 翻译研究正变得越来越重要，对基因组和观察学都有影响 研究、临床试验和比较效果研究。用于确定患者队列的关键组件 在电子病历中，定义纳入和排除标准，根据算法选择患者组 存储的临床数据。这一过程通常被称为“EHR驱动的表型”是耗时的 由于缺乏被广泛接受的、基于标准的正式信息模型来定义 表型算法。为了应对这一总体挑战，拟议的项目将设计、建造和推广 开放访问的社区基础设施，用于基于标准的开发和表型共享 算法，并为调查人员、研究人员及其信息学支持提供工具和资源 工作人员在原生EHR数据上实施和执行算法。

项目成果

Some experiences and opportunities for big data in translational research.

• DOI: 10.1038/gim.2013.121
• 发表时间: 2013-10
• 期刊: Genetics in medicine : official journal of the American College of Medical Genetics

Quantifying the importance of disease burden on perceived general health and depressive symptoms in patients within the Mayo Clinic Biobank.

• DOI: 10.1186/s12955-015-0285-6
• 发表时间: 2015-07-03
• 期刊: Health and quality of life outcomes
• 影响因子: 3.6
• 作者: Ryu E;Takahashi PY;Olson JE;Hathcock MA;Novotny PJ;Pathak J;Bielinski SJ;Cerhan JR;Sloan JA
• 通讯作者: Sloan JA

CQL4NLP: Development and Integration of FHIR NLP Extensions in Clinical Quality Language for EHR-driven Phenotyping.

CQL4NLP：在临床质量语言中开发和集成 FHIR NLP 扩展，以实现 EHR 驱动的表型分析。

• 发表时间: 2021
• 期刊: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
• 作者: Wen,Andrew;Rasmussen,LukeV;Stone,Daniel;Liu,Sijia;Kiefer,Rick;Adekkanattu,Prakash;Brandt,PascalS;Pacheco,JenniferA;Luo,Yuan;Wang,Fei;Pathak,Jyotishman;Liu,Hongfang;Jiang,Guoqian
• 通讯作者: Jiang,Guoqian

Integration of NLP2FHIR Representation with Deep Learning Models for EHR Phenotyping: A Pilot Study on Obesity Datasets.

NLP2FHIR 表示与 EHR 表型深度学习模型的集成：肥胖数据集的试点研究。

• 发表时间: 2021
• 期刊: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
• 作者: Liu,Sijia;Luo,Yuan;Stone,Daniel;Zong,Nansu;Wen,Andrew;Yu,Yue;Rasmussen,LukeV;Wang,Fei;Pathak,Jyotishman;Liu,Hongfang;Jiang,Guoqian
• 通讯作者: Jiang,Guoqian

Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project.

• DOI: 10.1016/j.jbi.2012.01.009
• 发表时间: 2012-08
• 期刊: Journal of biomedical informatics
• 影响因子: 4.5
• 作者: Rea S;Pathak J;Savova G;Oniki TA;Westberg L;Beebe CE;Tao C;Parker CG;Haug PJ;Huff SM;Chute CG
• 通讯作者: Chute CG