| Autor: |
Fabacher T; University hospital of Strasbourg, France.; ICube Laboratory, Strasbourg, France.; Inria Paris, France.; Centre de Recherche des Cordeliers, Inserm, Université Paris Cité, France., Sauleau EA; University hospital of Strasbourg, France.; ICube Laboratory, Strasbourg, France., Leclerc Du Sablon N; University hospital of Strasbourg, France., Bergier H; University hospital of Strasbourg, France., Gottenberg JE; University hospital of Strasbourg, France., Coulet A; Inria Paris, France.; Centre de Recherche des Cordeliers, Inserm, Université Paris Cité, France., Névéol A; Université Paris-Saclay, CNRS, LISN, Orsay, France. |
| Abstrakt: |
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. |
