Relationship Discovery for Drug Recommendation
Xiang Li Shunpan Liang Yu Lei Chen Li Yulei Hou Tengfei Ma
Medication recommendation systems are designed to deliver personalized drug
suggestions that are closely aligned with individual patient needs. Previous
studies have primarily concentrated on developing medication embeddings,
achieving significant progress. Nonetheless, these approaches often fall short
in accurately reflecting individual patient profiles, mainly due to challenges
in distinguishing between various patient conditions and the inability to
establish precise correlations between specific conditions and appropriate
medications. In response to these issues, we introduce DisMed, a model that
focuses on patient conditions to enhance personalization. DisMed employs causal
inference to discern clear, quantifiable causal links. It then examines patient
conditions in depth, recognizing and adapting to the evolving nuances of these
conditions, and mapping them directly to corresponding medications.
Additionally, DisMed leverages data from multiple patient visits to propose
combinations of medications. Comprehensive testing on real-world datasets
demonstrates that DisMed not only improves the customization of patient
profiles but also surpasses leading models in both precision and safety.