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<dc:title xml:lang="fr">L’intelligence artificielle au service de la pharmacie clinique a l’hôpital : construction d'algorithmes prédictifs d'aide à l'expertise pharmaceutique des prescriptions médicamenteuses</dc:title>
<dcterms:alternative xml:lang="en">Artificial intelligence for hospital clinical pharmacy : training predictive algorithms to assist pharmaceutical expertise of drug prescriptions</dcterms:alternative>
<dc:subject xml:lang="fr">Intelligence artificielle</dc:subject>
<dc:subject xml:lang="fr">Machine learning</dc:subject>
<dc:subject xml:lang="fr">Pharmacie clinique</dc:subject>
<dc:subject xml:lang="fr">Intervention pharmaceutique</dc:subject>
<dc:subject xml:lang="fr">Iatrogénie médicamenteuse</dc:subject>
<dc:subject xml:lang="en">Artificial intelligence</dc:subject>
<dc:subject xml:lang="en">Machine learning</dc:subject>
<dc:subject xml:lang="en">Clinical pharmacy</dc:subject>
<dc:subject xml:lang="en">Pharmaceutical intervention</dc:subject>
<dc:subject xml:lang="en">Adverse drug event</dc:subject>
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<tef:elementdEntree autoriteExterne="027313883" autoriteSource="Sudoc">Pharmacologie clinique</tef:elementdEntree>
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<tef:elementdEntree autoriteExterne="027313883" autoriteSource="Sudoc">Pharmacologie clinique</tef:elementdEntree>
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<dcterms:abstract xml:lang="fr">Ce projet de recherche mêlant l’intelligence artificielle à la pharmacie clinique a permis d’entrainer et de comparer les performances de trois modèles de machine learning : random forest, light gradient boosting machine et un réseau de neurones artificiels. L’entrainement de ces algorithmes s’est basé sur des données rétrospectives de patients hospitalisés dans un seul établissement sur une période de quatre ans. L’algorithme de random forest a présenté les meilleurs résultats en terme de F1-score. Il est capable de prédire la probabilité de nécessité d’une intervention pharmaceutique sur une ligne de prescription hospitalière. Les résultats sont satisfaisants et contribuent à l’avancée de la recherche en apportant de nouvelles perspectives dans le domaine de l’intelligence artificielle en pharmacie clinique hospitalière. Les travaux exploratoires continueront à enrichir ces algorithmes et contribueront ainsi à sécuriser la prise en charge médicamenteuse des patients.</dcterms:abstract>
<dcterms:abstract xml:lang="en">This research project combining artificial intelligence and clinical pharmacy involved training and comparing the performance of three machine learning models: random forest, light gradient boosting machine and an artificial neural network. The training of these algorithms was based on retrospective data from patients hospitalized in a single hospital over a four-year period. The random forest algorithm showed the best results in terms of F1-score. It is capable of predicting the probability of needing a pharmaceutical intervention on a hospital prescription line. The results are satisfactory and contribute to the progress of the research, bringing new perspectives to the field of artificial intelligence in hospital clinical pharmacy. Exploratory work will continue to enrich these algorithms, thereby contributing to safer patient medication management.</dcterms:abstract>
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