早期且准确地诊断脓毒症对于改善患者预后至关重要。本研究旨在利用心率变异性（HRV）特征开发一种有效的脓毒症检测预测模型。研究通过包括统计自助法（bootstrapping）和 Boruta 算法在内的特征工程方法识别关键 HRV 特征，随后采用不同的超参数设置训练 XGBoost 和 Random Forest 分类器。此外，研究构建了集成模型，以汇总高召回率分类器和高精度分类器的预测概率，从而提升模型性能。最后，研究基于 HRV 特征训练了一个神经网络模型，其 F1 分数达到 0.805，精确率为 0.851，召回率为 0.763。研究通过可解释性分析，将表现最佳的机器学习模型与该神经网络进行比较；其中实施了 Local Interpretable Model-agnostic Explanations（局部可解释模型无关解释，LIME），以依据特定特征的数值范围和阈值确定决策标准。本研究不仅突出了 HRV 在自动化脓毒症诊断中的功效，还提高了黑箱输出的透明度，从而最大化临床适用性。
英文摘要：
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.
Early Prediction of Sepsis using Heart Rate Signals and Genetic Optimized LSTM Algorithm
Department of Mechatronics, School of Intelligent Systems College of Interdisciplinary Science and Technology University of Tehran（机械工程系、智能系统学院 interdisciplinary 科学与技术学院 波斯语大学）；School of Science and Technology Faculty of Science, Agriculture, Business and Law University of New England（科学与技术学院 科学、农业、商业与法律学院 新英格兰大学）；School of Project Management Faculty of Engineering The University of Sydney（项目管理学院 工程学院 悉尼大学）
Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis
Northeastern University（东北大学）；Massachusetts Institute of Technology（麻省理工学院）；The Ohio State University（俄亥俄州立大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；The Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医学中心）
Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis
Heidelberg University Hospital（海德堡大学医院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学校）；Heidelberg University（海德堡大学）；University Hospital Essen（埃森大学医院）；University Duisburg-Essen（杜伊斯堡-埃森大学）
AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Heidelberg University Hospital（海德堡大学医院）；University Medical Center Mannheim（曼海姆大学医学中心）；DKFZ Hector Cancer Institute（DKFZ赫克托癌症研究所）
Early Prediction of Sepsis: Feature-Aligned Transfer Learning
School of Physical Therapy, Faculty of Health Sciences, Western University（物理治疗学院，健康科学学院，西部大学）；Department of Computer Science, Western University（计算机科学系，西部大学）；Health and Rehabilitation Sciences Faculty of Health Sciences, Western University（健康与康复科学学院，健康科学学院，西部大学）
Improving AI-Based Canine Heart Disease Diagnosis with Expert-Consensus Auscultation Labeling
Fraunhofer Institute for Software and Systems Engineering ISST（弗劳恩霍夫软件与系统工程研究所）；Department of Computer Science, TU Dortmund University（图林根大学计算机科学系）；Department of Clinical Sciences, Swedish University of Agricultural Sciences（瑞典农业科学大学临床科学系）；Clinic for Small Animal Medicine, Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学小动物医学诊所）；Boehringer Ingelheim Vetmedica GmbH（波士顿-英氏兽医医药有限公司）；Department of Health Informatics, School of Medicine, Witten/Herdecke University（乌尔姆-赫尔德克大学医学院健康信息学系）
Coronary Artery Disease Diagnosis; Ranking the Significant Features Using Random Trees Model
University of Birjand（比尔詹德大学）；Maragheh Faculty of Medical Sciences（马拉盖医科大学）；Queensland University of Technology（昆士兰科技大学）；Bauhaus Universität-Weimar（魏玛包豪斯大学）；Obuda University（奥布达大学）；Duy Tan University（越南大智大学）；Ton Duc Thang University（东德大学）
GSVMA: A Genetic-Support Vector Machine-Anova method for CAD diagnosis based on Z-Alizadeh Sani dataset
University of Birjand（比尔詹德大学）；Fasa University（法萨大学）；Deakin University（迪肯大学）；Maragheh Faculty of Medical Sciences（马拉盖医学院）；Obuda University（欧布达大学）；Technische Universität Dresden（德累斯顿工业大学）
Explainable Machine Learning Framework for Cardiovascular Disease Diagnosis and Prognosis
Engineering Bangladesh University, Dhaka-1207, Bangladesh.；Technology University, Santosh, Tangail-1902.；Engineering University of Information Technology \ Sciences, Dhaka, Bangladesh. moradul\；Dept. of Computer Science Maharishi International University, Fairfield, IA 52557, United States.；\ Bangladesh University, Dhaka-1207, Bangladesh.；a Deparment of Computer Science \& Engineering, Bangladesh University, Dhaka-1207, Bangladesh.；Tecnology University, Tangail-1902, Bangladesh.；c Deparment of Computer Science \& Engineering, University of Information Technology of Sciences, Dhaka, Bangladesh.；d Department of Computer Science,Fairfield, IA 52557, Maharishi International University, United States.
ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis
Zhejiang University School of Medicine（浙江大学医学院）；The First Affiliated Hospital（第一附属医院）；Liangzhu Laboratory（良渚实验室）；Shandong Jianzhu University（山东建筑大学）；FUXI AI Lab（伏羲人工智能实验室）；Beijing Normal University（北京师范大学）；Southern University of Science and Technology（南方科技大学）；China University of Geosciences (Wuhan)（中国地质大学（武汉））；Hangzhou Baorun Biotechnology Co., Ltd.（杭州宝润生物科技有限公司）
Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG
Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；School of Computing, National University of Singapore（新加坡国立大学计算学院）；National University of Singapore（新加坡国立大学）
A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era
University of Bremen（不来梅大学）；Imperial College London（伦敦帝国学院）；Griffith University（格里菲斯大学）；Beijing Institute of Technology（北京理工大学）；University of Augsburg（奥格斯堡大学）；Technical University of Munich（慕尼黑工业大学）；GLAM, Imperial College London（伦敦帝国学院GLAM机构）
MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network
State Key Laboratory of Transvascular Implantation Devices and TIDRI, Zhejiang University（浙江大学血管介入关键实验室及TIDRI）；School of Public Health, Zhejiang University and Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江大学公共卫生学院及浙江省医学影像人工智能重点实验室）；State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital and Liangzhu Laboratory, Zhejiang University（浙江大学第二附属医院血管介入关键实验室及梁渚实验室）；Department of Gastroenterology, First Hospital of Quanzhou Affiliated to Fujian Medical University（福建医科大学泉州第一医院消化内科）；School of Medicine, Stanford University（斯坦福大学医学院）
