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Debiased, Longitudinal and Coordinated Drug Recommendation through Multi-Visit Clinic Records
Hongda Sun · Shufang Xie · Shuqi Li · Yuhan Chen · Ji-Rong Wen · Rui Yan


AI-empowered drug recommendation has become an important task in healthcare research areas, which offers an additional perspective to assist human doctors with more accurate and more efficient drug prescriptions. Generally, drug recommendation is based on patients' diagnosis results in the electronic health records. We assume that there are three key factors to be addressed in drug recommendation: 1) elimination of recommendation bias due to limitations of observable information, 2) better utilization of historical health condition and 3) coordination of multiple drugs to control safety. To this end, we propose DrugRec, a causal inference based drug recommendation model. The causal graphical model can identify and deconfound the recommendation bias with front-door adjustment. Meanwhile, we model the multi-visit in the causal graph to characterize a patient's historical health conditions. Finally, we model the drug-drug interactions (DDIs) as the propositional satisfiability (SAT) problem, and solving the SAT problem can help better coordinate the recommendation. Comprehensive experiment results show that our proposed model achieves state-of-the-art performance on the widely used datasets MIMIC-III and MIMIC-IV, demonstrating the effectiveness and safety of our method.

Author Information

Hongda Sun (Gaoling School of Artificial Intelligence, Renmin University of China)
Shufang Xie (Renmin University of China)
Shuqi Li (Renmin University of China)
Yuhan Chen (Renmin University of China)
Ji-Rong Wen (Renmin University of China)
Rui Yan (Peking University)

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