PheBC: bias correction methods for EHR derived phenotype

项目概要 表型分析 药物 观察性的 到 记录 先进的 必填 指识别特定表型患者状态的过程，例如疾病状态、 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，表型分析很大程度上依赖于专家共识 为个体疾病创建表型定义。然而，随着电子医疗的广泛应用 （EHR）在临床研究和人工智能（AI）技术的发展中，更多 表型分析工作致力于自动特征提取，减少了手动工作 创建精确的表型。 t 那里 上 表型分析 为了 表型分析 我们的 表型 具体来说， 添加 我们 研究 获取和重用基于现有表型信息和算法的重大挑战 以可计算的方式进行电子健康记录（EHR）。此外，在我们当前的项目中， 信息提取系统和ias校正工具最初被设计为独立工具 为了研究目的，我们建议开发一套开放服务，以促进共享和重用 研究界的信息提取工具和偏差纠正工具。 该行政补充的总体目标是传播机器可读和可计算的 定义和算法，以减少重复工作并提高临床研究的可重复性。 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) 让研究团体参与促进偏差纠正工具的采用。 计划重构我们的软件架构和用户界面，以提高我们的工具的采用率 社区。 乙 我

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