{"doi":"10.1177/23969873251314340","title":"Automated extraction of post-stroke functional outcomes from unstructured electronic health records","abstract":"PURPOSE: Population level tracking of post-stroke functional outcomes is critical to guide interventions that reduce the burden of stroke-related disability. However, functional outcomes are often missing or documented in unstructured notes. We developed a natural language processing (NLP) model that reads electronic health records (EHR) notes to automatically determine the modified Rankin Scale (mRS). METHOD: We included consecutive patients (⩾18 years) with acute stroke admitted to our center (2015-2024). mRS scores were obtained from the Get With the Guidelines registry and clinical notes (if documented), and used as the gold standard to compare against NLP-generated scores. We used text-based features from notes, along with age, sex, discharge status, and outpatient follow-up to train a logistic regression for prediction of good (0-2) versus poor (3-6) mRS, and a linear regression for the full range of mRS scores. The models were trained for prediction of mRS at hospital discharge and post-discharge. The models were externally validated in a dataset of patients with brain injuries from a different healthcare center. FINDINGS: We included 5307 patients, 5006 in train and test and 301 in validation; average age was 69 (SD 15) and 65 (SD 17) years, respectively; 47% female. The logistic regression achieved an area under the receiver operating curve (AUROC) of 0.94 [CI 0.93-0.95] (test) and 0.94 [0.91-0.96] (validation), and the linear model a root mean squared error (RMSE) of 0.91 [0.87-0.94] (test) and 1.17 [1.06-1.28] (validation). DISCUSSION AND CONCLUSION: The NLP-based model is suitable for use in large-scale phenotyping of stroke functional outcomes and population health research.","journal":"European Stroke Journal","year":2025,"id":540156,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9279,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1376890,"name":"Kaileigh Gallagher","orcid":"0009-0005-2802-9790","position":1,"is_corresponding":false},{"id":1265254,"name":"Niels Turley","orcid":null,"position":2,"is_corresponding":false},{"id":1074947,"name":"Aditya Gupta","orcid":"0000-0002-5243-368X","position":3,"is_corresponding":false},{"id":280809,"name":"M. Brandon Westover","orcid":"0000-0003-4803-312X","position":4,"is_corresponding":false},{"id":379555,"name":"Aneesh B. Singhal","orcid":"0000-0002-2641-5277","position":5,"is_corresponding":false},{"id":485320,"name":"Sahar F. Zafar","orcid":"0000-0001-5252-5376","position":6,"is_corresponding":false},{"id":446095,"name":"Marta Fernandes","orcid":"0000-0002-7203-2832","position":0,"is_corresponding":true}],"reference_count":22,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:52:34.520788Z","pmid":"39838914","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}