Title :

AI-Driven Strategies for Precision and Efficiency in Optimising Medical Iatrogeny Detection

Author(s) :

Othman, Sarah Ben [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Laboratoire d'Automatique, Génie Informatique et Signal [LAGIS]
Ajmi, Faiza [Auteur]
Laboratoire Interdisciplinaire des transitions de Lille [LITL]
Decaudin, Bertrand [Auteur]
Groupe de Recherche sur les formes Injectables et les Technologies Associées - ULR 7365 [GRITA]
Odou, Pascal [Auteur]
Groupe de Recherche sur les formes Injectables et les Technologies Associées - ULR 7365 [GRITA]
Rousselière, Chloé [Auteur]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Cousein, Etienne [Auteur]
Groupe de Recherche sur les formes Injectables et les Technologies Associées - ULR 7365 [GRITA]
Hammadi, Slim [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Journal title :

2024 10th International Conference on Control, Decision and Information Technologies (CoDIT)

Publisher :

Print on Demand(PoD) ISSN: 2576-3547

Publication date :

2024-10-18

ISSN :

2576-3555

HAL domain(s) :

Informatique [cs]

English abstract : [en]

Medication iatrogeny is a significant patient safety challenge in the healthcare field. This issue pertains to the undesirable effects resulting from the use of drugs, including errors in prescribing, dosing, or administration. ...
Show more >Medication iatrogeny is a significant patient safety challenge in the healthcare field. This issue pertains to the undesirable effects resulting from the use of drugs, including errors in prescribing, dosing, or administration. In this context, the use of Machine Learning (ML) techniques to predict clinicaloutcomes is becoming increasingly common. The objective of this work is to develop a decision-support system designed to provide recommendations and assist pharmacists in analyzing prescriptions to reduce the risks associated with iatrogenic medication use for patients. ML algorithms are applied to classify prescriptions as valid or invalid using a MIMIC database containing patient medical data. We followed strict guidelines to process the data to improve model performance and then evaluated the model's performance using crossvalidation, referring to standard metrics. The system integrateswith existing hospital software, allowing pharmacists to receive recommendations and alerts for potential medication errors. We obtained an average accuracy of 96% for predicting the validity of medical prescriptions. Our study demonstrates that the use of ML algorithms for predicting the validity of medical prescriptions is an effective method. The results also suggest that diversifying the data could improve the model's performance. The findings of this study have valuable implications for clinical practice by providing a useful tool for the early detection of medication errors and could contribute to the enhancement of decision support systems in medicine.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL