Integrating clinical guidelines with large language models for improved sepsis mortality prediction

作者：Zhen Zhao, Bo An, Tianpeng Zhang, Ruixin Zhu, Zihao Fan, Guoxing Wang · 发表于：Health Informatics Journal · 年份：2025 · DOI：10.1177/14604582251387649 · 被引用次数：2 · 研究领域：Sepsis Diagnosis and Treatment、Machine Learning in Healthcare、Artificial Intelligence in Healthcare and Education

We develop and validate a clinical guideline-integrated LLM for enhanced sepsis mortality prediction. Using MIMIC-IV data from 24,237 ICU sepsis patients, we fine-tuned a large language model with Low-Rank Adaptation, embedding clinical guidelines into the training process. The model's predictive performance was evaluated using accuracy, F1-score, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Ablation studies assessed the specific contributions of clinical guideline integration. The guideline-enhanced fine-tuned LLM demonstrated moderately higher performance across all evaluation metrics including predictive accuracy (0.819), F1-score (0.815), sensitivity (0.815), specificity (0.822), and AUC (0.852) in predicting mortality risk for septic patients compared to traditional machine learning (highest accuracy: 0.774, AUC: 0.850) and deep learning methods (highest accuracy: 0.762, AUC: 0.841). Ablation experiments demonstrated that explicit integration of clinical guideline knowledge substantially improved performance over both direct prompting (accuracy: 0.709, AUC: 0.706) and fine-tuning without clinical guidelines (accuracy: 0.786, AUC: 0.801). These findings demonstrate that incorporating clinical guidelines into the fine-tuning of large language models outperforms both traditional and deep learning baselines across multiple metrics in sepsis mortality prediction, highlighting the value of explicit domain knowledge integration for...