TY  - JOUR
AU  - Gérardin, Christel
AU  - Mageau, Arthur
AU  - Mékinian, Arsène
AU  - Tannier, Xavier
AU  - Carrat, Fabrice
PY  - 2022
DA  - 2022/12/19
TI  - Construction of Cohorts of Similar Patients From Automatic Extraction of Medical Concepts: Phenotype Extraction Study
JO  - JMIR Med Inform
SP  - e42379
VL  - 10
IS  - 12
KW  - natural language processing
KW  - similar patient cohort
KW  - phenotype
KW  - systemic disease
KW  - NLP
KW  - algorithm
KW  - automatic extraction
KW  - automated extraction
KW  - named entity
KW  - MeSH
KW  - medical subject heading
KW  - data extraction
KW  - text extraction
AB  - 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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2022/12/e42379
UR  - https://doi.org/10.2196/42379
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36534446
DO  - 10.2196/42379
ID  - info:doi/10.2196/42379
ER  -