Debiasing Medication Recommendation with Counterfactual Analysis

The AI-driven medication recommendation has emerged as a crucial undertaking in the field of healthcare research. Recent literature has focused on leveraging patients' diagnoses, procedures, and historical visit information for medication recommendation. However, this approach can lead to recommendation biases due to spurious correlations among the historical visit information. Previous studies have either failed to address this bias issue or attempted to mitigate recommendation biases through dataset manipulation, albeit at the expense of increased computational costs. In this study, we propose CAMeR (Counterfactual Analysis based Medication Recommendation), which is a novel debiasing model based on counterfactual analysis. The model preserves medications information while emphasizing the primary influence of diagnoses and procedures. Unlike traditional factual reasoning approaches that address biases before or during training, counterfactual reasoning mitigates the impact of post-training spurious correlations. Additionally, we incorporate contrastive loss computation in the embedding module of our model to calibrate the feature construction for patients with multiple visit information. We validate the CAMeR on widely adopted datasets, MIMIC-III and MIMIC-IV, and experimental results unequivocally demonstrate its superiority over state-of-the-art methods.