Intensive Care Units usually carry patients with a serious risk of mortality. Recent research has shown the ability of Machine Learning to indicate the patients' mortality risk and point physicians toward individuals with a heightened need for care. Nevertheless, healthcare data is often subject to privacy regulations and can therefore not be easily shared in order to build Centralized Machine Learning models that use the combined data of multiple hospitals. Federated Learning is a Machine Learning framework designed for data privacy that can be used to circumvent this problem. In this study, we evaluate the ability of deep Federated Learning to predict the risk of Intensive Care Unit mortality at an early stage. We compare the predictive performance of Federated, Centralized, and Local Machine Learning in terms of AUPRC, F1-score, and AUROC. Our results show that Federated Learning performs equally well as the centralized approach and is substantially better than the local approach, thus providing a viable solution for early Intensive Care Unit mortality prediction. In addition, we show that the prediction performance is higher when the patient history window is closer to discharge or death. Finally, we show that using the F1-score as an early stopping metric can stabilize and increase the performance of our approach for the task at hand.
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）
Federated Learning with Multi-Partner OneFlorida+ Consortium Data for Predicting Major Postoperative Complications
Intelligent Clinical Care Center, University of Florida（佛罗里达大学智能临床护理中心）；Department of Medicine, Division of Nephrology, Hypertension, and Renal Transplantation, University of Florida（佛罗里达大学医学系肾病、高血压和肾移植分部）；Department of Surgery, University of Florida（佛罗里达大学外科系）；Department of Biomedical Engineering, University of Florida（佛罗里达大学生物医学工程系）
MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learning
Maastricht University Medical Centre +（马斯特里赫特大学医学中心）；Leiden University Medical Center（莱顿大学医学中心）；Delft University of Technology（代尔夫特理工大学）；Erasmus MC - University Medical Center Rotterdam（伊拉斯姆斯大学医学中心鹿特丹分校）；Leiden University（莱顿大学）
XAI for In-hospital Mortality Prediction via Multimodal ICU Data
Wuhan University（武汉大学）；University of Oxford（牛津大学）；The University of Sydney（悉尼大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Edinburgh（爱丁堡大学）
ICU Mortality Prediction Using Long Short-Term Memory Networks
Faculty of Medicine, University of Monastir（莫纳斯提尔大学医学院）；Higher Institute of Computer Sciences and Mathematics, University of Monastir（莫纳斯提尔大学高等计算机科学与数学学院）
Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Northeast Georgia Health System（东北乔治亚医疗系统）；Palos Health（帕洛斯医疗中心）；Penn State University（宾夕法尼亚州立大学）；Purdue University（普渡大学）
Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation
New Mexico Institute of Mining and Technology（新墨西哥矿业技术学院）；Oak Ridge National Laboratory（橡树岭国家实验室）；The University of Tennessee Health Science Center（田纳西大学健康科学中心）；Illinois State University（伊利诺伊州立大学）
Protocol Choices Matter More Than the Aggregation Algorithm: Federated Learning for In-Hospital Mortality Prediction in Care-Unit and Multi-Hospital Federations
Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte (IFRN)（北大河州教育、科学和技术联邦学院（IFRN））
Think as a Doctor: An Interpretable AI Approach for ICU Mortality Prediction
School of Information Management & Engineering, Shanghai University of Finance and Economics（上海金融学院信息管理与工程学院）；Key Laboratory of Data Intelligence and Management (Beihang University), Ministry of Industry and Information Technology, School of Economics and Management, Beihang University（北京航空航天大学数据智能与管理重点实验室）
Building Deep Learning Models to Predict Mortality in ICU Patients
George Washington University（乔治华盛顿大学）
重症监护病房的死亡率预测被认为是有效治疗重症患者的关键步骤之一。因此，已经开发了各种预测模型来基于现代电子健康护理记录来解决这个问题。然而，它变得越来越具有挑战性的建模任务作为时间序列变量，因为一些实验室测试结果，如心率和血压采样不一致的时间频率。在本文中，我们提出了几个深度学习模型，使用与SAPS II评分相同的特征。深入了解拟议模型的性能。基于众所周知的临床数据集Medical Information Mart for Intensive Care III进行了几个实验。预测结果表明，所提出的模型的能力，在精度，召回率，F1得分，和受试者工作特征曲线下的面积。
Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review
Affiliated Hospital of Guangdong Medical University（广东医科大学附属医院）；Maastricht University Medical Center（马斯特里赫特大学医学中心）；GROW School for Oncology and Reproduction, Maastricht University Medical Center（马斯特里赫特大学医学中心肿瘤与生殖GROW学院）；Institute of Nephrology, Affiliated Hospital of Guangdong Medical University（广东医科大学附属医院肾脏病研究所）；Guangdong Provincial Key Laboratory of Autophagy and Major Chronic Non-communicable Diseases（广东省自噬与重大慢性非传染性疾病重点实验室）；Beijing University of Technology（北京工业大学）；China Agricultural University（中国农业大学）；JTB Technology Corp.（JTB科技股份有限公司）；Guangdong Medical University（广东医科大学）；Chinese PLA General Hospital（中国人民解放军总医院）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）
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）
CT-HEG: A Bidirectional, Timestamp-Attributed Event Graph for ICU In-Hospital Mortality Prediction - An Architectural Ablation Study
Westcliff University（韦斯特克利夫大学）；Northern University Bangladesh（孟加拉国北方大学）；Stanton University（斯坦顿大学）；Washington University of Science and Technology（华盛顿科技大学）
Early Mortality Prediction in ICU Patients with Hypertensive Kidney Disease Using Interpretable Machine Learning
Department of Industrial and Systems Engineering, University of Southern California（美国南加州大学工业与系统工程系）；Colorectal Research Center, Iran University of Medical Sciences（伊朗医学科学大学结直肠研究中心）；Department of Health Science, California State University, Long Beach (CSULB)（加州大学长滩分校健康科学系）；Department of Industrial and Manufacturing Engineering, California State Polytechnic University, Pomona（加州州立大学波莫纳分校工业与制造工程系）
Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach
organization= Daniel J. Epstein Department of Industrial \& Systems Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= United States；organization= Colorectal Research Center, Iran University of Medical Sciences , city= Tehran , postcode= 14535 , country= Iran；organization= Department of Health Science, California State University , city= Long Beach , state= CA , postcode= 90840 , country= United States；organization= Department of Industrial；Manufacturing Engineering, California State Polytechnic University , city= Pomona , state= CA , postcode= 91768 , country= United States
Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data
Soochow University（苏州大学）；Advanced Institute of Information Technology, Peking University（北京大学信息技术高等研究院）；National Institute of Health Data Science, Peking University（北京大学健康医疗大数据国家研究院）；Peking University People’s Hospital（北京大学人民医院）
Development and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data
University of California, San Diego Health（加州大学圣地亚哥分校健康中心）；University of California, San Diego（加州大学圣地亚哥分校）
Improving Fairness of Large Language Model-Based ICU Mortality Prediction via Case-Based Prompting
School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China（天津理工大学计算机科学与工程学院）；Institute for Artificial Intelligence, Peking University, Beijing, China（北京大学人工智能研究院）；DCST, BNRist, RIIT, Institute of Internet Industry, Tsinghua University, Beijing, China（清华大学信息产业研究院）；National Institute of Health Data Science, Peking University, Beijing, China（北京大学健康数据科学国家研究院）
Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction
Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA（计算机科学与工程系，华盛顿大学圣路易斯分校）；Institute for Informatics, Washington University School of Medicine, St. Louis, MO, USA（信息学院，华盛顿大学医学学院）；Department of Anaesthesiology, Washington University School of Medicine, St. Louis, MO, USA（麻醉学系，华盛顿大学医学学院）；Department of Pulmonary Critical Care and Medicine, Washington University School of Medicine, St. Louis, MO, USA（呼吸科重症医学系，华盛顿大学医学学院）
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）
