Deep learning application to automated classification of recommendations made by hospital pharmacists during medication prescription review.

Fiche publication

Date publication

mai 2024

Journal

American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists

Auteurs

Membres identifiés du Cancéropôle Est :
Pr GODET Julien

Tous les auteurs :
Alkanj A, Godet J, Johns E, Gourieux B, Michel B

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/38294025

Résumé

Recommendations to improve therapeutics are proposals made by pharmacists during the prescription review process to address suboptimal use of medicines. Recommendations are generated daily as text documents but are rarely reused beyond their primary use to alert prescribers and caregivers. If recommendation data were easier to summarize, they could be used retrospectively to improve safeguards for better prescribing. The objective of this work was to train a deep learning algorithm for automated recommendation classification to valorize the large amount of recommendation data.