PheBC: bias correction methods for EHR derived phenotype
PheBC: bias correction methods for EHR derived phenotype
批准号:
10839649
负责人:
Yong Chen
金额:
$24.77万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-09-01 至 2025-05-31
关键词:
AddressAdministrative SupplementAdoptionAlgorithmsArchitectureArtificial IntelligenceClinicalClinical ResearchCloud ServiceCodeCommunitiesComplexComputer softwareConsensusConsumptionDataDevelopmentDiseaseDrug ExposureEcosystemElectronic Health RecordEnsureGoalsIndividualInformation RetrievalIntentionInternetKnowledgeKnowledge DiscoveryLearningLife Cycle StagesLiteratureLogicManualsMethodsObservational StudyPatientsPharmaceutical PreparationsPhenotypeProcessProtocols documentationPublishingPythonsReadabilityRecordsReproducibilityResearchResearch PersonnelServicesSoftware EngineeringSoftware ToolsSystemTechnologyTimeVisualizationcloud basedcohortcommunity engaged researchcomputable phenotypescomputing resourcesconcept mappingdeep learning algorithmdesigneHealthfeature extractionflexibilitygraphical user interfaceimprovedmachine learning modelmethod developmentopen sourceopen source libraryparent projectresearch and developmentsoftware developmenttooltreatment responseusabilityweb based interfaceweb services
中文摘要
项目摘要
表型
药物
观察性
到
记录
先进
需
是指鉴定特定表型患者状态,如疾病状态,
暴露和治疗反应。I是现实世界中最关键的数据提取任务之一
基于患者数据的研究。传统上,表型分析在很大程度上依赖于专家共识
为个别疾病创建表型定义。然而,随着电子健康的广泛使用,
(EHR)在临床研究和人工智能(AI)技术的发展,更多
表型分析的努力已经致力于自动化特征提取,减少了人工工作
来创造精确的表型
不
那里
对
表型
为
表型
我们
表型
具体地说,
添加
我们
研究
在获取和重用现有的表型信息和算法的基础上,
电子健康记录(EHR),以可计算的方式。此外,在我们目前的项目中,
信息提取系统和国际会计准则修正工具最初设计为独立工具
研究目的,我们建议开发一套开放的服务,促进共享和重用
研究界的信息提取工具和偏见纠正工具。
《行政补编》首要果阿是传播机读和可计算的
定义和算法,以减少重复工作,提高临床研究的再现性。
两个具体目标是:(1)通过以下方式增强表型信息提取工具的可重用性
API和服务。(2)让研究界参与进来,以促进偏见纠正工具的采用。
计划重构我们的软件架构和用户界面,以提高我们的工具在
社区.
B
L
英文摘要
PROJECT SUMMARY
Phenotyping
drug
observational
to
records
advanced
required
refers to the process of identifying specific phenotypic patient statuses, such as disease status,
exposure, and treatment response. I is one of the most critical data extraction tasks in real-world
studies based on patient data. Traditionally, phenotyping has heavily relied on expert consensus
create phenotype definitions for individual diseases. However, with the widespread usage of electronic health
(EHRs) in clinical research and the development of artificial intelligence (AI) technologies, more
phenotyping efforts have been devoted to automated feature extraction, reducing the manual effort
to create precise phenotypes.
t
There
on
phenotyping
for
phenotyping
Our
phenotype
Specifically,
adding
We
research
are significant challenges in acquiring and reusing existing phenotyping information and algorithms based
electronic health records (EHRs) in a computable manner. Furthermore, in our current project, the
information extraction system and ias correction tool were originally designed as stand-alone tools
research purposes, we propose to develop a set of open services that f acilitate the sharing and reuse of
information extraction tools and bias correction tools in research communities.
overarching goa of t his Administrative Supplement is to disseminate machine-readable and computable
definitions and algorithms to reduce duplication of effort and improve reproducibility in clinical studies.
the two specific aims are: (1) Enhance the reusability of phenotyping information extraction tools by
APIs and services. (2) Engage research communities to promote the adoption of bias correction tools.
plan to refactor our software architecture and user interfaces to enhance the adoption of our tools among
communities.
b
l
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
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海外基金