biostudies-literatureLiu LResearch Data InfrastructureAustralian Medical Research Futures Fund484-499https://www.ebi.ac.uk/biostudies/studies/S-EPMC12844598biostudies-literatureUnknown33(2)<h4>Objective</h4>Clinical registries advance healthcare by tracking patient outcomes and intervention safety. Manually extracting information from clinical text for registries is labor- and resource-intensive and often inaccurate. Therefore, this systematic review aims to evaluate the use and effectiveness of natural language processing (NLP) methods in extracting information from clinical text for populating clinical registries.<h4>Materials and methods</h4>PubMed, Embase, Scopus, Web of Science, and ACM Digital Library were systematically searched. Studies were included if they used NLP techniques to populate clinical registries. The extracted data included details of the registry, the clinical text, the registry data elements extracted, the NLP methods used, and how their performance wJournal of the American Medical Informatics Association : JAMIAUsing natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review.PMC12844598MRFFRD000154Kennedy GLiu LBarman MDelaney GPJorm LOoi SYChurches TBlake VGallego BfalseUsing natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review.<h4>Objective</h4>Clinical registries advance healthcare by tracking patient outcomes and intervention safety. Manually extracting information from clinical text for registries is labor- and resource-intensive and often inaccurate. Therefore, this systematic review aims to evaluate the use and effectiveness of natural language processing (NLP) methods in extracting information from clinical text for populating clinical registries.<h4>Materials and methods</h4>PubMed, Embase, Scopus, Web of Science, and ACM Digital Library were systematically searched. Studies were included if they used NLP techniques to populate clinical registries. The extracted data included details of the registry, the clinical text, the registry data elements extracted, the NLP methods used, and how their performance w2026-01-01T00:00:00Z2026 Feb2026-06-15T03:21:30.579Z2026-06-15T03:11:38.978ZS-EPMC128445984109329610.1093/jamia/ocaf176