Recommendation for New Drugs with Limited Prescription Data
Abstract
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.
Cite
Text
Wu et al. "Recommendation for New Drugs with Limited Prescription Data." NeurIPS 2022 Workshops: MetaLearn, 2022.Markdown
[Wu et al. "Recommendation for New Drugs with Limited Prescription Data." NeurIPS 2022 Workshops: MetaLearn, 2022.](https://mlanthology.org/neuripsw/2022/wu2022neuripsw-recommendation/)BibTeX
@inproceedings{wu2022neuripsw-recommendation,
title = {{Recommendation for New Drugs with Limited Prescription Data}},
author = {Wu, Zhenbang and Yao, Huaxiu and Su, Zhe and Liebovitz, David and Glass, Lucas M and Zou, James and Finn, Chelsea and Sun, Jimeng},
booktitle = {NeurIPS 2022 Workshops: MetaLearn},
year = {2022},
url = {https://mlanthology.org/neuripsw/2022/wu2022neuripsw-recommendation/}
}