Sepsis is a life-threatening organ malfunction caused by the host's inability to fight infection, which can lead to death without proper and immediate treatment. Therefore, early diagnosis and medical treatment of sepsis in critically ill populations at high risk for sepsis and sepsis-associated mortality are vital to providing the patient with rapid therapy. Studies show that advancing sepsis detection by 6 hours leads to earlier administration of antibiotics, which is associated with improved mortality. However, clinical scores like Sequential Organ Failure Assessment (SOFA) are not applicable for early prediction, while machine learning algorithms can help capture the progressing pattern for early prediction. Therefore, we aim to develop a machine learning algorithm that predicts sepsis onset 6 hours before it is suspected clinically. Although some machine learning algorithms have been applied to sepsis prediction, many of them did not consider the fact that six hours is not a small gap. To overcome this big gap challenge, we explore a multi-subset approach in which the likelihood of sepsis occurring earlier than 6 hours is output from a previous subset and feed to the target subset as additional features. Moreover, we use the hourly sampled data like vital signs in an observation window to derive a temporal change trend to further assist, which however is often ignored by previous studies. Our empirical study shows that both the multi-subset approach to alleviating the 6-hour gap and the added temporal trend features can help improve the performance of sepsis-related early prediction.
Peking University（北京大学）；Shanghai University of Finance and Economics（上海财经大学）
There are significant regional inequities in health resources around the world. It has become one of the most focused topics to improve health services for data-scarce hospitals and promote health equity through knowledge sharing among medical institutions. Because electronic medical records (EMRs) contain sensitive personal information, privacy protection is unavoidable and essential for multi-hospital collaboration. In this paper, for a common disease in ICU patients, sepsis, we propose a novel cross-center collaborative learning framework guided by medical knowledge, SofaNet, to achieve ear…
Emory University（埃默里大学）；Georgia Institute of Technology（佐治亚理工学院）
Although there has been significant research in boosting of weak learners, there has been little work in the field of boosting from strong learners. This latter paradigm is a form of weighted voting with learned weights. In this work, we consider the problem of constructing an ensemble algorithm from 70 individual algorithms for the early prediction of sepsis from clinical data. We find that this ensemble algorithm outperforms separate algorithms, especially on a hidden test set on which most algorithms failed to generalize.
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）
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）
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）
UC, Davis Health（加州大学戴维斯健康中心）；University of California, Santa Barbara（加州大学圣巴巴拉分校）
Sepsis is a leading cause of death in the Intensive Care Units (ICU). Early detection of sepsis is critical for patient survival. In this paper, we propose a multimodal Transformer model for early sepsis prediction, using the physiological time series data and clinical notes for each patient within $36$ hours of ICU admission. Specifically, we aim to predict sepsis using only the first 12, 18, 24, 30 and 36 hours of laboratory measurements, vital signs, patient demographics, and clinical notes. We evaluate our model on two large critical care datasets: MIMIC-III and eICU-CRD. The proposed meth…
Sepsis is an important cause of mortality, especially in intensive care unit (ICU) patients. Developing novel methods to identify early mortality is critical for improving survival outcomes in sepsis patients. Using the MIMIC-III database, we integrated demographic data, physiological measurements and clinical notes. We built and applied several machine learning models to predict the risk of hospital mortality and 30-day mortality in sepsis patients. From the clinical notes, we generated clinically meaningful word representations and embeddings. Supervised learning classifiers and a deep learn…
Georgia Institute of Technology（佐治亚理工学院）；Emory University School of Medicine（埃默里大学医学院）；University of California San Diego Health（加州大学圣地亚哥分校健康中心）
Sepsis, a dysregulated immune system response to infection, is among the leading causes of morbidity, mortality, and cost overruns in the Intensive Care Unit (ICU). Early prediction of sepsis can improve situational awareness amongst clinicians and facilitate timely, protective interventions. While the application of predictive analytics in ICU patients has shown early promising results, much of the work has been encumbered by high false-alarm rates. Efforts to improve specificity have been limited by several factors, most notably the difficulty of labeling sepsis onset time and the low preval…
MathWorks；Johns Hopkins University（约翰斯·霍普金斯大学）；MathWorks Natick MA USA（MathWorks 纳蒂克（美国马萨诸塞州））；Johns Hopkins University Baltimore MD USA（约翰斯·霍普金斯大学 巴尔的摩（美国马里兰州））
This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records. The Physionet 2019 challenge consists of medical records of over 40,000 patients. Using imputation and weak ensembler technique to analyze these medical records and 3-fold validation, a model is created and validated internally. The model achieved an accuracy of 93.45% and a utility score of 0.271. The utility score as defined by the organizers takes into account true positives, negatives and false alarms.
ETH Zurich（苏黎世联邦理工学院）；SIB Swiss Institute of Bioinformatics（瑞士生物信息学研究所）
Sepsis is a life-threatening host response to infection associated with high mortality, morbidity, and health costs. Its management is highly time-sensitive since each hour of delayed treatment increases mortality due to irreversible organ damage. Meanwhile, despite decades of clinical research, robust biomarkers for sepsis are missing. Therefore, detecting sepsis early by utilizing the affluence of high-resolution intensive care records has become a challenging machine learning problem. Recent advances in deep learning and data mining promise to deliver a powerful set of tools to efficiently …
NPCNet: Navigator-Driven Pseudo Text for Deep Clustering of Early Sepsis Phenotyping
Institute of Data Science and Engineering（数据科学与工程研究所）；National Yang Ming Chiao Tung University（阳明交通大学）；EECS International Graduate Program（电子工程国际研究生项目）；College of Intelligent Computing and Medical Statistics Research Center（智能计算与医学统计研究室）；Chang Gung University（长庚大学）；Department of Emergency Medicine（急诊医学科）；Department of Computer Science（计算机科学系）；Computational Health Informatics Program（计算健康信息学项目）
Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）；Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University（江苏大学附属昆山医院骨科部）；Tianjin University Chest Hospital（天津大学胸科医院）；Haihe Hospital, Tianjin University（天津大学海河医院）；Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University（广西师范大学教育区块链与智能技术重点实验室）
Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential data increase the possibility to predict sepsis and take early preventive treatment. However, the early prediction is challenging because patients' sequential data in EHR contains temporal interactions of multiple clinical events. And capturing temporal interactions in the long event sequence is hard for traditional LSTM. Rather than directly applyin…
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）
Peking University（北京大学）；Zhejiang University School of Medicine Second Affiliated Hospital（浙江大学医学院附属第二医院）；Beijing HealSci Technology Co., Ltd.（北京和华瑞博科技有限公司）；Shanghai University of Finance and Economics（上海财经大学）
Leveraging machine learning techniques for Sepsis early detection and diagnosis has attracted increasing interest in recent years. However, most existing methods require a large amount of labeled training data, which may not be available for a target hospital that deploys a new Sepsis detection system. More seriously, as treated patients are diversified between hospitals, directly applying a model trained on other hospitals may not achieve good performance for the target hospital. To address this issue, we propose a novel semi-supervised transfer learning framework based on optimal transport t…
ETH Zurich（苏黎世联邦理工学院）；SIB Swiss Institute of Bioinformatics（SIB瑞士生物信息学研究所）
Despite decades of clinical research, sepsis remains a global public health crisis with high mortality, and morbidity. Currently, when sepsis is detected and the underlying pathogen is identified, organ damage may have already progressed to irreversible stages. Effective sepsis management is therefore highly time-sensitive. By systematically analysing trends in the plethora of clinical data available in the intensive care unit (ICU), an early prediction of sepsis could lead to earlier pathogen identification, resistance testing, and effective antibiotic and supportive treatment, and thereby be…
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）
Enversion A/S（恩维申公司）；Aarhus University（奥胡斯大学）；Aarhus University Hospital（奥胡斯大学医院）；Horsens Regional Hospital（霍森斯地区医院）；Aarhus University School of Engineering（奥胡斯大学工程学院）
The timeliness of detection of a sepsis event in progress is a crucial factor in the outcome for the patient. Machine learning models built from data in electronic health records can be used as an effective tool for improving this timeliness, but so far the potential for clinical implementations has been largely limited to studies in intensive care units. This study will employ a richer data set that will expand the applicability of these models beyond intensive care units. Furthermore, we will circumvent several important limitations that have been found in the literature: 1) Models are evalu…
SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection
School of Systems Engineering and Computing, University of Valle（系统工程与计算学院，山谷大学）；Digital Medicine Unit, Balgrist University Hospital（数字医学单元，巴尔格里斯大学医院）；Nucleus-AI Research（核芯AI研究所）
Sepsis is a deadly condition affecting many patients in the hospital. Recent studies have shown that patients diagnosed with sepsis have significant mortality and morbidity, resulting from the body's dysfunctional host response to infection. Clinicians often rely on the use of Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and the Modified Early Warning Score (MEWS) to identify early signs of clinical deterioration requiring further work-up and treatment. However, many of these tools are manually computed and were not designed for automated computat…
Imperial College London（帝国理工学院）；ETH Zürich（苏黎世联邦理工学院）
With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospital mortality and an increasing concern in the ageing western world. Recently, medical and technological advances have helped re-define the illness criteria of this disease, which is otherwise poorly understood by the medical society. Together with the rise of widely accessible Electronic Health Records, the advances in data mining and complex nonlinear algorithms are a promising avenue for the early detection of sep…
End to End Autoencoder MLP Framework for Sepsis Prediction
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；State Key Laboratory of Public Big Data, Guizhou University（贵州大学公共大数据国家重点实验室）
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）
The proliferation of early diagnostic technologies, including self-monitoring systems and wearables, coupled with the application of these technologies on large segments of healthy populations may significantly aggravate the problem of overdiagnosis. This can lead to unwanted consequences such as overloading health care systems and overtreatment, with potential harms to healthy individuals. The advent of machine-learning tools to assist diagnosis -- while promising rapid and more personalised patient management and screening -- might contribute to this issue. The identification of overdiagnosi…
University of Colorado School of Medicine（科罗拉多大学医学院）；CU Data Science to Patient Value (D2V)（科罗拉多大学数据科学转化患者价值中心）；University of Colorado Health（科罗拉多大学医疗系统）；University of Colorado College of Nursing（科罗拉多大学护理学院）；Epic Corporation（Epic公司）；Colorado School of Public Health（科罗拉多公共卫生学院）
Interest in an electronic health record-based computational model that can accurately predict a patient's risk of sepsis at a given point in time has grown rapidly in the last several years. Like other EHR vendors, the Epic Systems Corporation has developed a proprietary sepsis prediction model (ESPM). Epic developed the model using data from three health systems and penalized logistic regression. Demographic, comorbidity, vital sign, laboratory, medication, and procedural variables contribute to the model. The objective of this project was to compare the predictive performance of the ESPM wit…
Heidelberg University（海德堡大学）；University of Manitoba（曼尼托巴大学）
Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health records where sepsis labels were defined automatically following established clinical criteria. We argue that the practice of incorporating the clinical criteria that are used to automatically define g…
Sepsis Prediction Using Graph Convolutional Networks over Patient-Feature-Value Triplets
College of Computer and Information Science（计算机与信息科学学院）；State Key Laboratory of Public Big Data（公共大数据国家重点实验室）；Department of Anesthesiology（麻醉科）；Key Laboratory of Perioperative Multi-organ Protection（围术期多器官保护关键实验室）
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）
Sepsis is a leading cause of mortality and critical illness worldwide. While robust biomarkers for early diagnosis are still missing, recent work indicates that hyperspectral imaging (HSI) has the potential to overcome this bottleneck by monitoring microcirculatory alterations. Automated machine learning-based diagnosis of sepsis based on HSI data, however, has not been explored to date. Given this gap in the literature, we leveraged an existing data set to (1) investigate whether HSI-based automated diagnosis of sepsis is possible and (2) put forth a list of possible confounders relevant for …
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）
CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction
Tianjin University（天津大学）；Tianjin Medical University General Hospital（天津医科大学总医院）；Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine（急诊医学科，邵氏医院，浙江大学医学院）
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）
Enversion A/S（Enversion公司）；Aarhus University（奥胡斯大学）；Horsens Regional Hospital（霍森斯地区医院）；Aarhus University Hospital（奥胡斯大学医院）
Objectives: To evaluate the consequences of the framing of machine learning risk prediction models. We evaluate how framing affects model performance and model learning in four different approaches previously applied in published artificial-intelligence (AI) models. Setting and participants: We analysed structured secondary healthcare data from 221,283 citizens from four Danish municipalities who were 18 years of age or older. Results: The four models had similar population level performance (a mean area under the receiver operating characteristic curve of 0.73 to 0.82), in contrast to the mea…
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）
Explainable Machine Learning for Sepsis Outcome Prediction Using a Novel Romanian Electronic Health Record Dataset
Carol Davila University of Medicine and Pharmacy, Faculty of Medicine（卡罗尔·达维拉医药大学医学院）；POLITEHNICA Bucharest National University of Science and Technology（布加勒斯特理工大学）；General Surgery Department, University Emergency Hospital of Bucharest（布加勒斯特大学急救医院普外科）；Innovation and eHealth Center, Carol Davila University of Medicine and Pharmacy（卡罗尔·达维拉医药大学创新与电子健康中心）
From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU
Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Berlin Institute of Health at Charité – Universitätsmedizin Berlin（柏林夏里特医学院柏林健康研究所）；Universitätsmedizin Greifswald（格赖夫斯瓦尔德大学医学院）；Birmingham City University（伯明翰城市大学）
University of Tennessee Health Science Center（田纳西大学健康科学中心）
Fever can provide valuable information for diagnosis and prognosis of various diseases such as pneumonia, dengue, sepsis, etc., therefore, predicting fever early can help in the effectiveness of treatment options and expediting the treatment process. This study aims to develop novel algorithms that can accurately predict fever onset in critically ill patients by applying machine learning technique on continuous physiological data. We analyzed continuous physiological data collected every 5-minute from a cohort of over 200,000 critically ill patients admitted to an Intensive Care Unit (ICU) ove…
Cincinnati Children's Hospital Medical Center（辛辛那提儿童医院医疗中心）；University of Cincinnati College of Medicine（辛辛那提大学医学院）；University of Cincinnati（辛辛那提大学）
Structural magnetic resonance imaging studies have shown that brain anatomical abnormalities are associated with cognitive deficits in preterm infants. Brain maturation and geometric features can be used with machine learning models for predicting later neurodevelopmental deficits. However, traditional machine learning models would suffer from a large feature-to-instance ratio (i.e., a large number of features but a small number of instances/samples). Ensemble learning is a paradigm that strategically generates and integrates a library of machine learning classifiers and has been successfully …
Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings
School of Computing, Mathematics and Engineering, Charles Sturt University（查尔斯·斯特特大学计算、数学与工程学院）；Department of Computer Science, Rensselaer Polytechnic Institute（伦斯勒理工学院计算机科学系）；Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b)（孟加拉国国际腹泻病研究中心卫生系统与人口研究部）；Technical Training Unit, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b)（孟加拉国国际腹泻病研究中心技术培训部）
Early prediction of patients at risk of clinical deterioration can help physicians intervene and alter their clinical course towards better outcomes. In addition to the accuracy requirement, early warning systems must make the predictions early enough to give physicians enough time to intervene. Interpretability is also one of the challenges when building such systems since being able to justify the reasoning behind model decisions is desirable in clinical practice. In this work, we built an interpretable model for the early prediction of various adverse clinical events indicative of clinical …
Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients
Old Dominion University（奥多明尼昂大学）；OhioHealth Riverside Methodist Hospital（俄亥俄健康河景卫理公会医院）；OhioHealth Research Institute（俄亥俄健康研究所）；OhioHealth（俄亥俄健康）；The James Cancer Hospital and Solove Research Institute, Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医学中心詹姆斯癌症医院及索洛夫研究所）
Machine Learning-based Early Detection of Potato Sprouting Using Electrophysiological Signals
Institute of Information Systems and Networking, University of Applied Sciences and Arts of Southern Switzerland（信息系统与网络研究所，应用科学与艺术南方瑞士大学）；Laboratory for Web Science, Fernfachhochschule Schweiz（网络科学实验室，费兰法赫大学）；University of Applied Sciences and Arts of Southern Switzerland（应用科学与艺术南方瑞士大学）；Agroscope, Swiss Confederation’s Center for Agricultural Research（农业研究瑞士联邦中心）
