Leveraging the Long-Term Health Monitoring Dataset for Personalized Healthcare: A Large Language Model Approach

Abstract

Large Language Models (LLMs) have showcased exceptional capabilities in understanding and generating natural language, particularly in the realm of personalized healthcare assistants. Despite these advancements, LLMs face challenges with long-term memory, limiting their effectiveness in continuous interactions and personalized recommendations. To address these limitations, we present two main contributions: (1) a novel Long-Term Health Monitoring (LTHM) dataset, which contains 30 longitudinal patient profiles with comprehensive health records, symptoms tracking, and time-stamped dialogue interactions, specifically designed for evaluating LLMs' long-term memory capabilities in healthcare scenarios, and (2) a Memory Reflection and Dynamic Retrieval Weights (RTD) mechanism to enhance LLM performance in long-term interaction scenarios. Our LTHM dataset fills a critical gap in healthcare dialogue systems by providing detailed patient-LLM conversations across various chronic conditions, enabling robust evaluation of long-term memory mechanisms. Experimental results on our LTHM dataset demonstrate that the RTD strategy achieves outstanding performance, with a correctness score of 0.786 and a coherence score of 0.764, significantly outperforming baseline approaches. The effectiveness of our approach is further validated on the MemoryBank dataset, demonstrating its applicability across different dialogue scenarios. Our dataset and the corresponding code have been released on GitHub*.

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