PheBC: bias correction methods for EHR derived phenotype

点击翻译按钮获取中文摘要

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