Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series

TL;DR

This paper introduces MPP, a method that integrates metabolic pathway information into large language models to enhance anomaly detection in clinical time-series, demonstrated through doping detection in athletes.

Contribution

The paper presents a novel approach that incorporates metabolic pathway knowledge into LLMs, improving their ability to detect anomalies in biological data.

Findings

Enhanced detection accuracy with metabolic context integration

Improved identification of suspicious biological patterns

Effective application in doping detection for athletes

Abstract

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.