Enhancing Medication Recommendation with LLM Text Representation

TL;DR

This paper introduces a method that leverages Large Language Models to utilize unstructured clinical notes, enhancing medication recommendation systems by combining text and structured data, leading to improved performance across multiple models.

Contribution

The paper proposes a novel approach using LLMs for extracting information from unstructured clinical notes to improve medication recommendation accuracy.

Findings

LLM text representation alone performs comparably to medical code representation.

Combining text and code data improves recommendation performance.

Method is applicable to various existing models.

Abstract

Most of the existing medication recommendation models are predicted with only structured data such as medical codes, with the remaining other large amount of unstructured or semi-structured data underutilization. To increase the utilization effectively, we proposed a method of enhancing medication recommendation with Large Language Model (LLM) text representation. LLM harnesses powerful language understanding and generation capabilities, enabling the extraction of information from complex and lengthy unstructured data such as clinical notes which contain complex terminology. This method can be applied to several existing base models we selected and improve medication recommendation performance with the combination representation of text and medical codes experiments on two different datasets. LLM text representation alone can even demonstrate a comparable ability to the medical code representation alone. Overall, this is a general method that can be applied to other models for improved recommendations.