The early and accurate diagnosis of sepsis is critical for enhancing patient outcomes. This study aims to use heart rate variability (HRV) features to develop an effective predictive model for sepsis detection. Critical HRV features are identified through feature engineering methods, including statistical bootstrapping and the Boruta algorithm, after which XGBoost and Random Forest classifiers are trained with differential hyperparameter settings. In addition, ensemble models are constructed to pool the prediction probabilities of high-recall and high-precision classifiers and improve model performance. Finally, a neural network model is trained on the HRV features, achieving an F1 score of 0.805, a precision of 0.851, and a recall of 0.763. The best-performing machine learning model is compared to this neural network through an interpretability analysis, where Local Interpretable Model-agnostic Explanations are implemented to determine decision-making criterion based on numerical ranges and thresholds for specific features. This study not only highlights the efficacy of HRV in automated sepsis diagnosis but also increases the transparency of black box outputs, maximizing clinical applicability.

本研究针对脓毒症早期准确诊断的挑战，提出利用心率变异性（HRV）特征建立有效的预测模型。通过特征工程方法识别关键HRV特征，并使用XGBoost、随机森林及集成模型提升诊断性能，最终实现F1分数0.805，突显HRV在脓毒症自动诊断中的有效性及模型结果的透明性。

通过心率变异性改善基于机器学习的脓毒症诊断