Sepsis is a condition caused by the body's overwhelming and life-threatening response to infection, which can lead to tissue damage, organ failure, and finally death. Common signs and symptoms include fever, increased heart rate, increased breathing rate, and confusion. Sepsis is difficult to predict, diagnose, and treat. Patients who develop sepsis have an increased risk of complications and death and face higher health care costs and longer hospitalization. Today, sepsis is one of the leading causes of mortality among populations in intensive care units (ICUs). In this paper, we look at the problem of early detection of sepsis by using temporal data mining. We focus on the use of knowledge-based temporal abstraction to create meaningful interval-based abstractions, and on time-interval mining to discover frequent interval-based patterns. We used 2,560 cases derived from the MIMIC-III database. We found that the distribution of the temporal patterns whose frequency is above 10% discovered in the records of septic patients during the last 6 and 12 hours before onset of sepsis is significantly different from that distribution within a similar period, during an equivalent time window during hospitalization, in the records of non-septic patients. This discovery is encouraging for the purpose of performing an early diagnosis of sepsis using the discovered patterns as constructed features.
On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning
King's College London（伦敦国王学院）；University College London（伦敦大学学院）；University of Edinburgh（爱丁堡大学）；East and North Hertfordshire NHS Trust（东赫特福德郡和北赫特福德郡国民保健信托基金会）；University of Augsburg（奥格斯堡大学）；Luigi Sacco Hospital（路易吉·萨科医院）
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis
Technische Hochschule Nürnberg Georg Simon Ohm（纽伦堡乔治·西蒙·欧姆应用技术大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University of Regensburg（雷根斯堡大学）；Leipzig University（莱比锡大学）
A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs
Chengdu Medical College（成都医学院）；Kunming Medical University（昆明医科大学）；National Yang Ming Chiao Tung University（国立阳明交通大学）；University of Southern California（南加州大学）；Yanshan University（燕山大学）
MEET-Sepsis: Multi-Endogenous-View Enhanced Time-Series Representation Learning for Early Sepsis Prediction
Guangdong University of Technology（广东技术大学）；Women and Children's Medical Center of Southern Medical University（南方医科大学妇女儿童医疗中心）；Shenzhen Maternity and Child Healthcare Hospital（深圳妇幼保健医院）；Shenzhen BH-Energy Technology Co., Ltd.（深圳BH能源科技有限公司）；Hong Kong Baptist University（香港 Baptist大学）
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）
A Novel Multi-Task Teacher-Student Architecture with Self-Supervised Pretraining for 48-Hour Vasoactive-Inotropic Trend Analysis in Sepsis Mortality Prediction
University of Southern California（南加州大学）；California State University, Long Beach（加州州立大学长滩分校）；Iran University of Medical Sciences（伊朗医科大学）；California State Polytechnic University, Pomona（加州州立理工大学波莫纳分校）
Rare Event Early Detection: A Dataset of Sepsis Onset for Critically Ill Trauma Patients
University of Washington Tacoma WA USA（华盛顿大学塔科马分校）；Department of Statistics University of Washington Seattle WA USA（华盛顿大学统计学系）；Department of Surgery University of Washington Seattle WA USA（华盛顿大学外科系）
Machine Learning-Based Prediction of ICU Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU Database
University of Southern California（南加州大学）；Iran University of Medical Sciences（伊朗医学科学大学）；California State University（加利福尼亚州立大学）；California State Polytechnic University（加利福尼亚州_polytechnic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）
Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project
University of New South Wales（新南威尔士大学）；Australian National University（澳大利亚国立大学）；The George Institute for Global Health（乔治全球健康研究院）；Imperial College London（伦敦帝国理工学院）
EHR-MPC: Inference-Time Control for Sepsis Treatment with Generative Patient Digital Twins
Broad Institute of MIT and Harvard（MIT和哈佛大学Broad研究所）；Harvard University（哈佛大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Technion–Israel Institute of Technology（技术学院-以色列理工学院）
Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality
University of Southern California（南加州大学）；California State Polytechnic University, Pomona（加州州立理工大学波莫纳分校）；California State University, Long Beach（加州州立大学长滩分校）
Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients
强化学习（RL）的潜力已经通过在围棋和雅达利等游戏中的成功应用得到了证明。然而，虽然在游戏环境中通过简单地使用RL算法玩游戏来评估RL算法的性能是简单的，但在临床环境中评估是一个主要挑战，在临床环境中，在实践中遵循RL策略可能是不安全的。因此，了解RL政策对实施过程中所做决策的敏感性是建立最终临床应用所需的RL信任类型的重要一步。在这项工作中，我们对最先进的RL算法（Dueling Double Deep Q-Networks）进行了敏感性分析，该算法适用于ICU中脓毒症患者的血流动力学稳定治疗策略。我们考虑了学习策略对输入特征、嵌入模型结构、时间离散化、奖励函数和随机种子的敏感性。我们发现，改变这些设置可以显着影响学习的政策，这表明在解释RL代理输出时需要谨慎。
Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome
Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Vall d'Hebron University Hospital（瓦尔·德希布伦大学医院）；Eurecat, Centre Tecnològic de Catalunya（加泰罗尼亚技术中心Eurecat）；Instituto de Salud Carlos III（卡洛斯三世健康研究所）
Stochastic Parrots or ICU Experts? Large Language Models in Critical Care Medicine: A Scoping Review
National Institute of Health Data Science, Peking University（北京大学健康医疗大数据国家研究院）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Advanced Institute of Information Technology, Peking University（北京大学信息技术高等研究院）；Peking University Third Hospital（北京大学第三医院）；University of Liverpool（利物浦大学）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）；University of California, Los Angeles（加州大学洛杉矶分校）；Peking University People’s Hospital（北京大学人民医院）
Transformer representation learning is necessary for dynamic multi-modal physiological data on small-cohort patients
Nanjing University（南京大学）；The Affiliated Drum Tower Hospital of Nanjing University Medical School（南京大学医学院附属鼓楼医院）；National Laboratory of Solid State Microstructure（固体微结构物理国家重点实验室）
本文提出结合 Transformer 表征与传统机器学习的 POD 预测框架，利用多模态生理数据构建首个两类患者数据集，证明多模态 Transformer 表征可提升术后 1–3 天谵妄诊断性能。
