AKI-Detector: A Multi-Agent Framework by Integrating Machine Learning and Large Language Models for Early Prediction...

Acute kidney injury (AKI) is a severe condition in the ICU, where early prediction is crucial for timely intervention and prevention. Traditional machine learning (ML) models lack interpretability, which limits real-world applicability. We propose AKI-Detector, a novel multi-agent framework that integrates structured electronic health records (EHR)-based ML models, large language models (LLMs), and retrieval-augmented generation (RAG) to enhance clinical reasoning, accuracy, and interpretability of AKI prediction. The proposed AKI-Detector mitigates LLM hallucinations by integrating ML models and bridges the gap between algorithmic output and clinically interpretable reports. Evaluated on ICU data from MIMIC-IV, AKI-Detector outperformed ML models such as CatBoost and GRU, and achieved an accuracy of 0.827, precision of 0.672, recall of 0.542, and F1-score of 0.600 on the test cohort, demonstrating balanced and reliable predictive performance. This work highlights the promise of real-world big data and LLM-powered multi-agent systems to support trustworthy and explainable AI for clinical prediction.