From General to Specific: Tailoring Large Language Models for Personalized Healthcare

TL;DR

This paper introduces PMLM, a personalized medical language model that tailors responses to individual patients using recommendation systems and reinforcement learning, improving personalization in healthcare applications.

Contribution

The paper presents a novel personalized LLM framework that combines personalized prompts and reinforcement learning to enhance individual-specific medical responses.

Findings

PMLM achieves personalized responses in obstetrics and gynecology data.

It provides more refined and individualized medical services.

The method demonstrates high adaptability and reusability of prompts.

Abstract

The rapid development of large language models (LLMs) has transformed many industries, including healthcare. However, previous medical LLMs have largely focused on leveraging general medical knowledge to provide responses, without accounting for patient variability and lacking true personalization at the individual level. To address this, we propose a novel method called personalized medical language model (PMLM), which explores and optimizes personalized LLMs through recommendation systems and reinforcement learning (RL). Specifically, by utilizing self-informed and peer-informed personalization, PMLM captures changes in behaviors and preferences to design initial personalized prompts tailored to individual needs. We further refine these initial personalized prompts through RL, ultimately enhancing the precision of LLM guidance. Notably, the personalized prompt are hard prompt, which grants PMLM high adaptability and reusability, allowing it to directly leverage high-quality proprietary LLMs. We evaluate PMLM using real-world obstetrics and gynecology data, and the experimental results demonstrate that PMLM achieves personalized responses, and it provides more refined and individualized services, offering a potential way for personalized medical LLMs.