Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la eSanté, LIMICS, F-75006 Paris, France.
Institute of Applied Biosciences, Centre for Research and Development Hellas, Thessaloniki, Greece.
Stud Health Technol Inform. 2024 Aug 22;316:803-807. doi: 10.3233/SHTI240533.
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.
因果深度学习/机器学习(CDL/CML)是一种新兴的人工智能(AI)范式。因果推理和 AI 的结合可以挖掘数据特征之间可解释的因果关系,为各种应用提供有用的见解,例如基于真实世界数据的药物警戒(PV)信号检测。本研究的目的是展示使用 CDL 利用电子健康记录作为输入数据源进行潜在的 PV 信号验证。