Deep Learning Methods for Detecting Side Effects of Cancer Chemotherapies Reported in a Remote Monitoring Web Application.

Autor:	Metzger MH; Equipe soins primaires et prévention, INSERM U1018, Villejuif, France.; Service de médecine interne, Hôpital Antoine-Béclère, Assistance Publique Hôpitaux de Paris, Clamart, France., Gadji A; Equipe soins primaires et prévention, INSERM U1018, Villejuif, France., Haj Salah N; Equipe soins primaires et prévention, INSERM U1018, Villejuif, France., Kane W; Equipe soins primaires et prévention, INSERM U1018, Villejuif, France., Boue F; Service de médecine interne, Hôpital Antoine-Béclère, Assistance Publique Hôpitaux de Paris, Clamart, France.
Jazyk:	angličtina
Zdroj:	Studies in health technology and informatics [Stud Health Technol Inform] 2022 May 25; Vol. 294, pp. 880-881.
DOI:	10.3233/SHTI220616
Abstrakt:	The objective of our work was to develop deep learning methods for extracting and normalizing patient-reported free-text side effects in a cancer chemotherapy side effect remote monitoring web application. The F-measure was 0.79 for the medical concept extraction model and 0.85 for the negation extraction model (Bi-LSTM-CRF). The next step was the normalization. Of the 1040 unique concepts in the dataset, 62, 3% scored 1 (corresponding to a perfect match with an UMLS CUI). These methods need to be improved to allow their integration into home telemonitoring devices for automatic notification of the hospital oncologists.
Databáze:	MEDLINE
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