Validating Emergency Department Admission Predictions Based on Local Data Through MIMIC-IV
Hellenic Open University（希腊开放大学）；Sismanogleio General Hospital（西斯马诺格利奥综合医院）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚大学）；Amalia Fleming General Hospital（阿玛利亚·弗莱明综合医院）
The effective management of Emergency Department (ED) overcrowding is essential for improving patient outcomes and optimizing healthcare resource allocation. This study validates hospital admission prediction models initially developed using a small local dataset from a Greek hospital by leveraging the comprehensive MIMIC-IV dataset. After preprocessing the MIMIC-IV data, five algorithms were evaluated: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Random Forest (RF), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM Radial). Among these, RF demonstrated superior performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.9999, sensitivity of 0.9997, and specificity of 0.9999 when applied to the MIMIC-IV data. These findings highlight the robustness of RF in handling complex datasets for admission prediction, establish MIMIC-IV as a valuable benchmark for validating models based on smaller local datasets, and provide actionable insights for improving ED management strategies.
Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
Harvard Medical School（哈佛医学院）；Stanford University（斯坦福大学）；Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）
Benchmarking emergency department triage prediction models with machine learning and large public electronic health records
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Nanyang Technological University（南洋理工大学）；Monash University（莫纳什大学）；National University of Singapore（新加坡国立大学）；Duke University（杜克大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）；Singapore General Hospital（新加坡中央医院）；Singapore Health Services（新加坡保健服务集团）
Small Language Models for Emergency Departments Decision Support: A Benchmark Study
Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada（电气与软件工程系，卡尔加里大学）；Department of Emergency Medicine, University of Calgary, Calgary, AB, Canada（急诊医学系，卡尔加里大学）；Rockview General Hospital, Calgary, AB, Canada（罗克维尔医院）
Emergency Department Optimization and Load Prediction in Hospitals
KenSci Inc（KenSci公司）；University of Washington（华盛顿大学）；Swedish Medical Center（瑞典医疗中心）；University of Washington - Bothell（华盛顿大学博塞尔分校）；University of Washington - Tacoma（华盛顿大学塔科马分校）
An Integrated Forecasting Prototype for Emergency Department Boarding Time to Support Proactive Operational Decision Making
Department of Electrical and Computer Engineering, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校电气与计算机工程系）；Department of Health Services Administration, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校卫生服务管理系）；Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校希尔斯医院医学院生物医学信息学与数据科学系）；Department of Emergency Medicine, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校急诊医学系）
Using machine learning techniques to predict hospital admission at the emergency department
Hellenic Open University（希腊开放大学）；Sismanogleio General Hospital（西斯马诺格利奥综合医院）；Thoracic Diseases General Hospital Sotiria（索提里亚胸科疾病综合医院）；National and Kapodistrian University of Athens（雅典国家与卡波迪斯特里安大学）
Development of a Large Language Model-based Multi-Agent Clinical Decision Support System for Korean Triage and Acuity Scale (KTAS)-Based Triage and Treatment Planning in Emergency Departments
John Bapst Memorial High School（约翰·巴普斯特纪念高中）；Minerva University（密涅瓦大学）
Forecasting mortality associated emergency department crowding
Faculty of Medicine and Health Technology, Tampere University（坦佩雷大学医学与健康技术学院）；Emergency Department, Tampere University Hospital（坦佩雷大学医院急诊科）；Kanta-Häme Central Hospital（坎塔-海梅中心医院）；Faculty of Information Technology and Communication Sciences, Tampere University（坦佩雷大学信息技术与传播科学学院）；Centre for Vascular Surgery and Interventional Radiology, Tampere University Hospital（坦佩雷大学医院血管外科与介入放射中心）
Development and Comparative Evaluation of Three Artificial Intelligence Models (NLP, LLM, JEPA) for Predicting Triage in Emergency Departments: A 7-Month Retrospective Proof-of-Concept
Department of Emergency Lille University Hospital（里尔大学医院急诊科）；Centre Antipoison, Lille University Hospital（里尔大学医院毒物中心）；Department of Public Health, EA 2694 & ULR 2694-METRICS（公共卫生部门，EA 2694及ULR 2694-METRICS）；Lille University（里尔大学）；Emergency Department Hôpital Pitié-Salpêtrière, AP-HP（皮蒂埃-萨尔佩特里耶医院急诊科，AP-HP）
ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance
Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）；University of California, Berkeley（加州大学伯克利分校）；University of California, San Francisco（加州大学旧金山分校）；Department of EECS, University of California, Berkeley（加州大学伯克利分校电子工程与计算机科学系）
Multi-Modal Perceiver Language Model for Outcome Prediction in Emergency Department
Qatar Computing Research Institute（卡塔尔计算研究所）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；LIMTIC Laboratory（LIMTIC实验室）；UTM University（突尼斯马纳大学）；Higher institute of computer science of Medenine（梅德宁高等计算机科学学院）；Gabes University（加贝斯大学）；Sidra Medicine（锡德拉医学中心）
A unified machine learning approach to time series forecasting applied to demand at emergency departments
MRC Centre for Global Infectious Disease Analysis, Imperial College London（帝国理工学院MRC全球传染病分析中心）；Imperial College Healthcare NHS Trust（帝国理工学院医疗保健国民健康服务体系信托）；Faculty of Medicine, Imperial College London（帝国理工学院医学院）；Department of Infectious Disease, Imperial College London（帝国理工学院传染病系）；Department of Mathematics, Imperial College London（帝国理工学院数学系）
Analyzing the Variations in Emergency Department Boarding and Testing the Transferability of Forecasting Models across COVID-19 Pandemic Waves in Hong Kong: Hybrid CNN-LSTM approach to quantifying building-level socioecological risk
JC School of Public Health and Primary Care, The Chinese University of Hong Kong（香港中文大学赛马会公共卫生及基层医疗学院）；Hong Kong Polytechnic University（香港理工大学）
Effective Predictive Modeling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting
Canberra Health Services, Canberra Hospital（堪培拉卫生服务局，堪培拉医院）；University of Technology Sydney（悉尼科技大学）；University of New South Wales（新南威尔士大学）；The Australian National University（澳大利亚国立大学）；Obuda University（欧布达大学）
Detection of sepsis during emergency department triage using machine learning
Mednition Inc.（麦迪逊公司）；Sutter Mills-Peninsula Medical Center（萨特米尔半岛医疗中心）；Ascension Health（阿森松医疗）；Adventist Health White Memorial（基督复临安息日会怀特纪念医疗中心）；San Mateo Medical Center（圣马特奥医疗中心）；El Camino Hospital（埃尔卡米诺医院）；Emergency Nurses Association（急诊护士协会）；Duke University（杜克大学）
Improving Emergency Department ESI Acuity Assignment Using Machine Learning and Clinical Natural Language Processing
Mednition Inc.（Mednition公司）；Emergency Nurses Association（急诊护士协会）；El Camino Hospital（埃尔卡米诺医院）；San Mateo Medical Center（圣马特奥医疗中心）；Adventist Health White Memorial（复临健康怀特纪念医院）；Ascension Health（阿森松医疗）；American Osteopathic Association（美国骨科协会）
Quantifying the Impact of Ambulance Ramping: A Multi-Year Analysis of Victorian Emergency Medical Services Cases
The Centre of Excellence in Paramedicine (CoEP), Victoria University（卓越急救医学中心（CoEP），维多利亚大学）；Center for Research and Evaluation Ambulance Victoria（维多利亚救护车研究与评估中心）
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Sorbonne University（索邦大学）；Stanford University（斯坦福大学）；Singapore General Hospital（新加坡中央医院）；National University of Singapore（新加坡国立大学）
An Empirical Study of Machine Learning Robustness and Scalability for Imbalanced Tabular Clinical Data in Emergency and Critical Care
Computer Vision Group, Institute of Cognitive Science, Osnabrück University（计算机视觉组，认知科学研究所，奥斯纳布吕克大学）；Department of Mathematics, Rhodes University（数学系，罗德斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS)（国家理论与计算科学研究所（NITheCS））
