A causal machine learning framework for pharmacovigilance signal detection in electronic health records: Drug-induced acute kidney injury

An increasing number of studies in the field of pharmacovigilance have been testing different Artificial intelligence (AI) approaches on Real-World Data (RWD). These studies use conventional AI to predict adverse drug reactions by detecting the correlation of patient characteristics with adverse effects (AE). Typically, these studies do not aim to establish any causal relationship between the suspect drugs and the AE. Causal inference enables machine learning methods to estimate the treatment effect of medical interventions using RWD. However, applying causal inference to RWD presents its own set of challenges. Therefore, a comprehensive framework is essential. In this study, the integration of PRINCIPLED, a process guide for causal inference using healthcare data, electronic health records from the MIMIC-IV database, and Causal Machine Learning (CML) to detect drug-induced acute kidney injury, demonstrates a framework that can provide interpretable, reproducible, and clinically relevant information. The results position CML as a promising approach for improving the accuracy, transparency, and regulatory acceptance of pharmacovigilance systems.