Construction of Cohorts of Similar Patients From Automatic Extraction of Medical Concepts: Phenotype Extraction Study
| Autor: | Christel Gérardin, Arthur Mageau, Arsène Mékinian, Xavier Tannier, Fabrice Carrat |
|---|---|
| Přispěvatelé: | LEJEUNE, Emeline, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Infection, Anti-microbiens, Modélisation, Evolution (IAME (UMR_S_1137 / U1137)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Université Sorbonne Paris Nord, CHU Saint-Antoine [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Sorbonne Université (SU), Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord |
| Rok vydání: | 2022 |
| Předmět: |
MeSH
Automatic extraction [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] Similar patient cohort Natural language processing Data extraction [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS] Systemic disease Named entity [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS] Health Informatics Automated extraction Text extraction [INFO] Computer Science [cs] NLP Medical subject heading Algorithm Phenotype Health Information Management [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB] [INFO]Computer Science [cs] |
| Zdroj: | JMIR Medical Informatics JMIR Medical Informatics, 2022, 10 (12), pp.e42379. ⟨10.2196/42379⟩ |
| ISSN: | 2291-9694 |
| DOI: | 10.2196/42379⟩ |
| Popis: | Background Reliable and interpretable automatic extraction of clinical phenotypes from large electronic medical record databases remains a challenge, especially in a language other than English. Objective We aimed to provide an automated end-to-end extraction of cohorts of similar patients from electronic health records for systemic diseases. Methods Our multistep algorithm includes a named-entity recognition step, a multilabel classification using medical subject headings ontology, and the computation of patient similarity. A selection of cohorts of similar patients on a priori annotated phenotypes was performed. Six phenotypes were selected for their clinical significance: P1, osteoporosis; P2, nephritis in systemic erythematosus lupus; P3, interstitial lung disease in systemic sclerosis; P4, lung infection; P5, obstetric antiphospholipid syndrome; and P6, Takayasu arteritis. We used a training set of 151 clinical notes and an independent validation set of 256 clinical notes, with annotated phenotypes, both extracted from the Assistance Publique-Hôpitaux de Paris data warehouse. We evaluated the precision of the 3 patients closest to the index patient for each phenotype with precision-at-3 and recall and average precision. Results For P1-P4, the precision-at-3 ranged from 0.85 (95% CI 0.75-0.95) to 0.99 (95% CI 0.98-1), the recall ranged from 0.53 (95% CI 0.50-0.55) to 0.83 (95% CI 0.81-0.84), and the average precision ranged from 0.58 (95% CI 0.54-0.62) to 0.88 (95% CI 0.85-0.90). P5-P6 phenotypes could not be analyzed due to the limited number of phenotypes. Conclusions Using a method close to clinical reasoning, we built a scalable and interpretable end-to-end algorithm for extracting cohorts of similar patients. |
| Databáze: | OpenAIRE |
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