EDGE: KNOWLEDGE-DRIVEN NEW DRUG RECOM-MENDATION

Abstract

Drug recommendation assists doctors in prescribing personalized medications to patients based on their health conditions. Existing drug recommendation solutions adopt the supervised multi-label classification setup and only work with existing drugs with sufficient prescription data from many patients. However, newly approved drugs do not have much historical prescription data and cannot leverage existing drug recommendation methods. To address this, we formulate the new drug recommendation as a few-shot learning problem. Yet, directly applying existing few-shot learning algorithms faces two challenges: (1) complex relations among diseases and drugs and (2) numerous false-negative patients who were eligible but did not yet use the new drugs. To tackle these challenges, we propose EDGE, which can quickly adapt to the recommendation for a new drug with limited prescription data from a few support patients. EDGE maintains a drugdependent multi-phenotype few-shot learner to bridge the gap between existing and new drugs. Specifically, EDGE leverages the drug ontology to link new drugs to existing drugs with similar treatment effects and learns ontology-based drug representations. Such drug representations are used to customize the metric space of the phenotype-driven patient representations, which are composed of a set of phenotypes capturing complex patient health status. Lastly, EDGE eliminates the false-negative supervision signal using an external drug-disease knowledge base. We evaluate EDGE on two real-world datasets: the public EHR data (MIMIC-IV) and private industrial claims data. Results show that EDGE achieves 7.3% improvement on the ROC-AUC score over the best baseline.

1. INTRODUCTION

With the wide adoption of electronic health records (EHR) and the advance of deep learning models, we have seen great opportunities in assisting clinical decisions with deep learning models to improve resource utilization, healthcare quality, and patient safety (Xiao et al., 2018) . Drug recommendation is one of the essential applications which aims at assisting doctors in recommending personalized medications to patients based on their health conditions. Existing drug recommendation methods typically formulate it as a supervised multi-label classification problem (Zhang et al., 2017; Zitnik et al., 2018; Shang et al., 2019b; Yang et al., 2021; Rui Wu & Wu., 2022; Tan et al., 2022b) . They often train on massive prescription data to learn patient representations and use the learned representations to predict medications (i.e., labels). However, in reality, new drugs come to the market all the time. For example, U.S. Food and Drug Administration (FDA) approves a wide range of new drugs every year (FDA, 2022) . Most of these newly approved drugs do not have much historical data to support model training (Blass, 2021) . Even if sufficient prescription data for new drugs exists, existing models must be periodically re-trained or updated to recommend new drugs, which is expensive and complex. As a result, existing drug recommendation methods can only recommend the same set of drugs seen during training and are no longer applicable when new drugs appear. To address this, we formulate the recommendation of new drugs as a few-shot classification problem. Given a new drug with limited prescription data from a few support patients (e.g., from clinical trials (Duijnhoven et al., 2013) ), the model should quickly adapt to the recommendation for this drug. Meta-learning approaches have been widely used in such problems by learning how to quickly adapt the classifier to a new label unseen during training, given only a few support examples (Finn et al., 2017; Snell et al., 2017) . However, most prior meta-learning works focus on vision or language-

