From Indicators to Insights: Diversity-Optimized for Medical Series-Text Decoding via LLMs
Xiyuan Jin, Jing Wang, Ziwei Lin, QIANRU JIA, Yuqing Huang, Xiaojun Ning, Zhonghua Shi, Youfang Lin
Medical time-series analysis differ fundamentally from general ones by requiring specialized domain knowledge to interpret complex signals and clinical context. Large language models (LLMs) hold great promise for augmenting medical time-series analysis by complementing raw series with rich contextual knowledge drawn from biomedical literature and clinical guidelines. However, this potential can only be realized with prompts that are constructed precisely and meaningfully, guiding the LLM’s attention to the most relevant aspects of the data. Yet, determining what constitutes effective prompt content remains non-trivial—especially in medical settings where signal interpretation often hinges on subtle, expert-defined decision-making indicators. To address this challenge, we introduce InDiGO, a knowledge-aware evolutionary learning framework that systematically integrates clinical signals and decision-making indicators, continuously optimizing through iterative learning. Experiments across four real-world medical benchmarks show that InDiGO consistently outperforms existing state-of-the-art methods. The code is available at: https://anonymous.4open.science/r/InDiGO-5034.