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Construction of Cohorts of Similar Patients From Automatic Extraction of Medical Concepts: Phenotype Extraction Study.
- Source :
-
JMIR medical informatics [JMIR Med Inform] 2022 Dec 19; Vol. 10 (12), pp. e42379. Date of Electronic Publication: 2022 Dec 19. - Publication Year :
- 2022
-
Abstract
- 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.<br />Objective: We aimed to provide an automated end-to-end extraction of cohorts of similar patients from electronic health records for systemic diseases.<br />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.<br />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.<br />Conclusions: Using a method close to clinical reasoning, we built a scalable and interpretable end-to-end algorithm for extracting cohorts of similar patients.<br /> (©Christel Gérardin, Arthur Mageau, Arsène Mékinian, Xavier Tannier, Fabrice Carrat. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 19.12.2022.)
Details
- Language :
- English
- ISSN :
- 2291-9694
- Volume :
- 10
- Issue :
- 12
- Database :
- MEDLINE
- Journal :
- JMIR medical informatics
- Publication Type :
- Academic Journal
- Accession number :
- 36534446
- Full Text :
- https://doi.org/10.2196/42379