将代谢通路知识融入大模型，提升临床时间序列异常检测精度
Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series
- 通过代谢通路驱动的提示机制，增强大模型对生物样本结构与时间变化的理解
- 在运动员类固醇代谢数据上，显著提升可疑样本检测准确率
- 适合需要结合医学知识进行异常分析的研究者和医疗AI开发者
临床时间序列中的异常检测在识别不同生物参数中的可疑模式方面具有重要潜力。本文提出一种针对性方法，将临床领域知识融入大模型以增强其异常检测能力。我们引入代谢通路驱动提示（MPP）方法，整合代谢通路信息，更有效地捕捉生物样本的结构与时间变化。该方法应用于体育兴奋剂检测，聚焦类固醇代谢，基于真实运动员数据进行评估。结果表明，通过利用代谢上下文信息，本方法显著提升了异常检测性能，实现了对运动员个体特征中可疑样本更细致、更准确的预测。
原文摘要 · Abstract (English)
Anomaly detection in clinical time-series holds significant potential in identifying suspicious patterns in different biological parameters. In this paper, we propose a targeted method that incorporates the clinical domain knowledge into LLMs to improve their ability to detect anomalies. We introduce the Metabolism Pathway-driven Prompting (MPP) method, which integrates the information about metabolic pathways to better capture the structural and temporal changes in biological samples. We applied our method for doping detection in sports, focusing on steroid metabolism, and evaluated using real-world data from athletes. The results show that our method improves anomaly detection performance by leveraging metabolic context, providing a more nuanced and accurate prediction of suspicious samples in athletes' profiles.
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