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Large Language Model Distilling Medication Recommendation Model
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The recommendation of medication is a vital aspect of intelligent healthcare systems, as it involves prescribing the most suitable drugs based on a patient's specific health needs. Unfortunately, many sophisticated models currently in use tend to overlook the nuanced semantics of medical data, while only relying heavily on identities. Furthermore, these models face significant challenges in handling cases involving patients who are visiting the hospital for the first time, as they lack prior prescription histories to draw upon. To tackle these issues, we harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs). Our research aims to transform existing medication recommendation methodologies using LLMs. In this paper, we introduce a novel approach called Large Language Model Distilling Medication Recommendation (LEADER). We begin by creating appropriate prompt templates that enable LLMs to suggest medications effectively. However, the straightforward integration of LLMs into recommender systems leads to an out-of-corpus issue specific to drugs. We handle it by adapting the LLMs with a novel output layer and a refined tuning loss function. Although LLM-based models exhibit remarkable capabilities, they are plagued by high computational costs during inference, which is impractical for the healthcare sector. To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model. Extensive experiments conducted on two real-world datasets, MIMIC-III and MIMIC-IV, demonstrate that our proposed model not only delivers effective results but also is efficient. To ease the reproducibility of our experiments, we release the implementation code online.
Forward citations
Cited by 8 Pith papers
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EAG-RL improves LLM performance on EHR mortality and readmission prediction by training on expert-generated reasoning traces and an attention-alignment RL reward.
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ARMR: Adaptively Responsive Network for Medication Recommendation
A medication recommendation model that separates new from existing drugs and uses a piecewise temporal encoder with Mamba to improve prediction on MIMIC-III and MIMIC-IV.
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An alternating distillation loop between a conventional recommender and an LLM recommender improves top-K accuracy on four datasets without adding inference-time parameters.
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