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

Maxx Richard Rahman, Ruoxuan Liu, Wolfgang Maass

Keywords: LLMs, Clinical Time-Series, Anomaly Detection, Steroid Metabolism, Doping, Sports

Abstract:

Anomaly detection in clinical time-series holds significant potential in identifying suspicious patterns in different biological parameters. This paper proposes a targeted method that incorporates the clinical domain knowledge into LLMs to improve their ability to detect anomalies. The Metabolism Pathway-driven Prompting (MPP) approach is introduced, 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 an improved prediction of suspicious samples in athletes' profiles.

Submission Number: 48