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Recommendation for New Drugs with Limited Prescription Data
Zhenbang Wu · Huaxiu Yao · Zhe Su · David Liebovitz · Lucas Glass · James Zou · Chelsea Finn · Jimeng Sun

@
in NeurIPS 2022 Workshop on Meta-Learning »
Event URL: https://openreview.net/forum?id=AKxzQc9uTV »

Drug recommendation assists doctors in prescribing personalized medications to patients based on their health conditions. However, newly approved drugs do not have much historical prescription data and cannot leverage existing drug recommendation methods. To address this, we propose EDGE, which maintains a drug-dependent multi-phenotype few-shot learner to bridge the gap between existing and new drugs. Experiment results show that EDGE can adapt to the recommendation for a new drug with limited prescription data from a few patients.

Author Information

Zhenbang Wu (University of Illinois Urbana Champaign)
Huaxiu Yao (Stanford University)
Zhe Su (Zhejiang University)
David Liebovitz (Northwestern University)
Lucas Glass (IQVIA)
James Zou (Stanford)
Chelsea Finn (Stanford)
Jimeng Sun (University of Illinois, Urbana Champaign)

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