Fiche publication
Date publication
mai 2023
Journal
Studies in health technology and informatics
Auteurs
Membres identifiés du Cancéropôle Est :
Pr SAULEAU Erik-André
,
Pr GOTTENBERG Jacques-Eric
Tous les auteurs :
Fabacher T, Sauleau EA, Leclerc Du Sablon N, Bergier H, Gottenberg JE, Coulet A, Névéol A
Lien Pubmed
Résumé
Previous work has successfully used machine learning and natural language processing for the phenotyping of Rheumatoid Arthritis (RA) patients in hospitals within the United States and France. Our goal is to evaluate the adaptability of RA phenotyping algorithms to a new hospital, both at the patient and encounter levels. Two algorithms are adapted and evaluated with a newly developed RA gold standard corpus, including annotations at the encounter level. The adapted algorithms offer comparably good performance for patient-level phenotyping on the new corpus (F1 0.68 to 0.82), but lower performance for encounter-level (F1 0.54). Regarding adaptation feasibility and cost, the first algorithm incurred a heavier adaptation burden because it required manual feature engineering. However, it is less computationally intensive than the second, semi-supervised, algorithm.
Mots clés
Natural Language Processing, Phenotyping, Rheumatoid Arthritis
Référence
Stud Health Technol Inform. 2023 05 18;302:768-772
