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

arXiv: https://arxiv.org/abs/2410.12830 · HTML: https://mlsanity.com/paper/2410.12830/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science](https://mlsanity.com/paper/2403.14565/) (2024)
- [More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering](https://mlsanity.com/paper/2311.09782/) (2023)
- [Clinical Insights: A Comprehensive Review of Language Models in Medicine](https://mlsanity.com/paper/2408.11735/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare](https://mlsanity.com/paper/2502.15871/) (2025)
- [MetaBench: A Multi-task Benchmark for Assessing LLMs in Metabolomics](https://mlsanity.com/paper/2510.14944/) (2025)

## Abstract (arXiv)

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.