This study proposes the use of Machine Learning models to predict the early onset of sepsis using deidentified clinical data from Montefiore Medical Center in Bronx, NY, USA. A supervised learning approach was adopted, wherein an XGBoost model was trained utilizing 80\% of the train dataset, encompassing 107 features (including the original and derived features). Subsequently, the model was evaluated on the remaining 20\% of the test data. The model was validated on prospective data that was entirely unseen during the training phase. To assess the model's performance at the individual patient level and timeliness of the prediction, a normalized utility score was employed, a widely recognized scoring methodology for sepsis detection, as outlined in the PhysioNet Sepsis Challenge paper. Metrics such as F1 Score, Sensitivity, Specificity, and Flag Rate were also devised. The model achieved a normalized utility score of 0.494 on test data and 0.378 on prospective data at threshold 0.3. The F1 scores were 80.8\% and 67.1\% respectively for the test data and the prospective data for the same threshold, highlighting its potential to be integrated into clinical decision-making processes effectively. These results bear testament to the model's robust predictive capabilities and its potential to substantially impact clinical decision-making processes.
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…
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）
Reconstructing Sepsis Trajectories from Clinical Case Reports using LLMs: the Textual Time Series Corpus for Sepsis
Machine Learning Department and Heinz College Carnegie Mellon University（卡内基梅隆大学机器学习系和海恩兹学院）；National Library of Medicine National Institutes of Health（美国国家医学图书馆）
Enhancing LLMs' Clinical Reasoning with Real-World Data from a Nationwide Sepsis Registry
Korea Advanced Institute of Science and Technology（韩国科学技术院）；Microsoft（微软）；Asan Medical Center, University of Ulsan College of Medicine（釜山大学医学院阿桑医疗中心）；Samsung Medical Center, Sungkyunkwan University School of Medicine（成均馆大学医学院三星医疗中心）；Seoul National University Bundang Hospital, Seoul National University College of Medicine（首尔国立大学医学院首尔国立大学医院）
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）
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 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…
Learning Preference-Based Objectives from Clinical Narratives for Dynamic Sepsis Treatment
Institute of Data Science, National University of Singapore, Singapore（新加坡国立大学数据科学研究所）；National University Hospital, Singapore（新加坡国立大学医院）；Saw Swee Hock School of Public Health, National University of Singapore, Singapore（新加坡国立大学 Saw Swee Hock 公共卫生学院）
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…
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…
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）
Our aim is to establish a framework where reinforcement learning (RL) of optimizing interventions retrospectively allows us a regulatory compliant pathway to prospective clinical testing of the learned policies in a clinical deployment. We focus on infections in intensive care units which are one of the major causes of death and difficult to treat because of the complex and opaque patient dynamics, and the clinically debated, highly-divergent set of intervention policies required by each individual patient, yet intensive care units are naturally data rich. In our work, we build on RL approache…
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…
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）
Splunk Inc.（Splunk公司）；Georgia Institute of Technology（佐治亚理工学院）
We design and implement a temporal convolutional network model to predict sepsis onset. Our model is trained on data extracted from MIMIC III database, based on a retrospective analysis of patients admitted to intensive care unit who did not fall under the definition of sepsis at the time of admission. Benchmarked with several machine learning models, our model is superior on this binary classification task, demonstrates the prediction power of convolutional networks for temporal patterns, also shows the significant impact of having longer look back time on sepsis prediction.
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）
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…
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…
University of Pittsburgh（匹兹堡大学）；University of Pittsburgh School of Medicine（匹兹堡大学医学院）；Carnegie Mellon University（卡内基梅隆大学）；McGowan Institute for Regenerative Medicine（麦高恩再生医学研究所）
Sepsis is a potentially life threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the patient's state to guide the management of intravenous fluids and vasopressors, among other interventions. Despite decades of research, there's still debate among experts on optimal treatment. Here, we combine for the first time, distributional deep reinforcement learning with mechanistic physiological models to find personalized sepsis treatment strategies. Our method handles partial observability by leveraging know…
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）
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）
These two synthetic datasets comprise vital signs, laboratory test results, administered fluid boluses and vasopressors for 3,910 patients with acute hypotension and for 2,164 patients with sepsis in the Intensive Care Unit (ICU). The patient cohorts were built using previously published inclusion and exclusion criteria and the data were created using Generative Adversarial Networks (GANs) and the MIMIC-III Clinical Database. The risk of identity disclosure associated with the release of these data was estimated to be very low (0.045%). The datasets were generated and published as part of the …
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）
IMEDACS；Vivace Health Solutions Inc（Vivace健康解决方案公司）；Johns Hopkins University（约翰斯·霍普金斯大学）；Scripps Memorial Hospital - Encinitas（斯克里普斯恩西尼塔斯纪念医院）
Although timely sepsis diagnosis and prompt interventions in Intensive Care Unit (ICU) patients are associated with reduced mortality, early clinical recognition is frequently impeded by non-specific signs of infection and failure to detect signs of sepsis-induced organ dysfunction in a constellation of dynamically changing physiological data. The goal of this work is to identify patient at risk of life-threatening sepsis utilizing a data-centered and machine learning-driven approach. We derive a mortality risk predictive dynamic Bayesian network (DBN) guided by a customized sepsis knowledgeba…
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…
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 …
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）
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…
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）
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）
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…
The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate the performance of an RL algorithm in a game setting by simply using it to play the game, evaluation is a major challenge in clinical settings where it could be unsafe to follow RL policies in practice. Thus, understanding sensitivity of RL policies to the host of decisions made during implementation is an important step toward building the type of trust in RL required for eventual clinical uptake. In this work, we pe…
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）
