Revisiting Drug Recommendation From a Causal Perspective.

Abstract

Drug recommendation that aims to provide a prescription for a patient is an essential task in healthcare. Drug molecular graphs provide valuable support for drug recommendation. Existing methods tend to overlook drugs' molecular graphs or use the core substructures of molecular graphs with a rule-based segmentation strategy. However, such methods have several limitations: (1) The rule-based segmentation strategy is inflexible and sub-optimal for extremely complex scenarios. (2) The core substructures derived only consider the drug's chemical characteristics and ignore the patient's health condition. (3) The spurious correlation brought by trivial substructures is disregarded. To address these limitations, we design a novel drug recommendation method from a causal perspective, where a conditional causal representation learner for drug recommendation is proposed. Specifically, we first separate the drug molecular representation into causal and spurious parts depending on various patients' health conditions. Then, we eliminate the spurious correlation caused by the spurious part with causal intervention. Extensive experiments on the MIMIC-III and MIMIC-IV datasets demonstrate that our approach achieves new state-of-the-art performance (e.g., 6.68% Jaccard improvements on MIMIC-III with p-value << 0.05). Source codes are available at https://github.com/junjzhang7/CRec.