Causal Deep Learning for the Detection of Adverse Drug Reactions: Drug-Induced Acute Kidney Injury as a Case Study.
| Autor: | Dimitsaki S; Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la eSanté, LIMICS, F-75006 Paris, France., Natsiavas P; Institute of Applied Biosciences, Centre for Research and Development Hellas, Thessaloniki, Greece., Jaulent MC; Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la eSanté, LIMICS, F-75006 Paris, France. |
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| Jazyk: | angličtina |
| Zdroj: | Studies in health technology and informatics [Stud Health Technol Inform] 2024 Aug 22; Vol. 316, pp. 803-807. |
| DOI: | 10.3233/SHTI240533 |
| Abstrakt: | Causal Deep/Machine Learning (CDL/CML) is an emerging Artificial Intelligence (AI) paradigm. The combination of causal inference and AI could mine explainable causal relationships between data features, providing useful insights for various applications, e.g. Pharmacovigilance (PV) signal detection upon Real-World Data. The objective of this study is to demonstrate the use of CDL for potential PV signal validation using Electronic Health Records as input data source. |
| Databáze: | MEDLINE |
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