Responding rapidly to a patient who is demonstrating signs of imminent clinical deterioration is a basic tenet of patient care. This gave rise to a patient safety intervention philosophy known as a Rapid Response System (RRS), whereby a patient who meets a pre-determined set of criteria for imminent clinical deterioration is immediately assessed and treated, with the goal of mitigating the deterioration and preventing intensive care unit (ICU) transfer, cardiac arrest, or death. While RRSs have been widely adopted, multiple systematic reviews have failed to find evidence of their effectiveness. Typically, RRS criteria are simple, expert (consensus) defined rules that identify significant physiologic abnormalities or are based on clinical observation.
If one can find a pattern in the patient's data earlier than the onset of the physiologic derangement manifest in the current criteria, intervention strategies might be more effective. In this paper, we apply machine learning to electronic medical records (EMR) to infer if patients are at risk for clinical deterioration. Our models are more sensitive and offer greater advance prediction time compared with existing rule-based methods that are currently utilized in hospitals. Our results warrant further testing in the field; if successful, hospitals can integrate our approach into their existing IT systems and use the alerts generated by the model to prevent ICU transfer, cardiac arrest, or death, or to reduce the ICU length of stay.
Early Warning Index for Patient Deteriorations in Hospitals
Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）；Operations and Information Management, University of Wisconsin Madison（威斯康星大学麦迪逊分校运营与信息管理系）；Technology Management and Innovation, New York University（纽约大学技术管理与创新系）；Hartford HealthCare（哈特福德医疗集团）；Holistic Hospital Optimization（综合医院优化）
Improving Hospital Risk Prediction with Knowledge-Augmented Multimodal EHR Modeling
Department of Computer Science, University of Virginia（大学计算机科学系）；University of Virginia School of Medicine（弗吉尼亚大学医学院）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院和州立大学）；Biocomplexity Institute and Initiative, University of Virginia（大学生物复杂性研究所）；Division of Infectious Diseases & International Health, University of Virginia School of Medicine（大学感染性疾病与国际卫生分会）
A Machine Learning Early Warning System: Multicenter Validation in Brazilian Hospitals
Laura Health（劳拉健康）；PUCRS（巴西天主教大学里约热内卢分校（注：PUCRS常见全称为Pontifícia Universidade Católica do Rio Grande do Sul，即南里约热内卢天主教大学，此处保留缩写并给出常见译名））；University of Évora（埃武拉大学）
A Multimodal Deep Learning Framework for Predicting ICU Deterioration: Integrating ECG Waveforms with Clinical Data and Clinician Benchmarking
AI4Health Division, Carl von Ossietzky Universität Oldenburg（AI4Health部门，奥尔登堡大学）；CAPASITS, Secretariat of Health of the State of Colima（CAPASITS，科利马州卫生秘书处）；Department of Interventional Cardiology, Hospital Puerta de Hierro Colima（介入心脏病学部，科利马医院）；Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein（医学信息学与统计研究所，基尔大学和石勒苏益格-荷尔斯泰因大学医院）；Department of Anesthesiology, Intensive Care Medicine, Emergency Medicine, and Pain Therapy, Carl von Ossietzky Universität & Klinikum Oldenburg（麻醉学、重症医学、急诊医学和疼痛治疗部，奥尔登堡大学及奥尔登堡诊所）；Department of Cardiology, Angiology and Intensive Care Medicine, Charité Campus Mitte, German Heart Center of the Charité-University Medicine Berlin（心内科、血管科和重症医学部，Charité梅尔特校区，Charité-大学医学中心柏林德国心脏中心）
Temporal Data Requirement for Predicting Unplanned Hospital Readmissions
Northeastern University（东北大学）；Partners Healthcare Connected Health Innovation（Partners Healthcare 连接健康创新）；MGH Institute for Technology Assessment（MGH 技术评估研究所）；Harvard Medical School（哈佛医学院）；Tufts University School of Medicine（塔夫茨大学医学院）；Department of Public Health and Community Medicine（公共卫生与社区医学系）
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）
Hospital transfer risk prediction for COVID-19 patients from a medicalized hotel based on Diffusion GraphSAGE
Institute of Hospital and Health Care Administration（医院与健康照护管理研究所）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Far Eastern Memorial Hospital（远东纪念医院）
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）
Scalable Clinical Data Infrastructure and Comparative ML Evaluation for Hospitalisation Risk Prediction in Elderly Patients with Multiple Long-Term Conditions using CPRD
School of Information, University of Sheffield（谢菲尔德大学信息学院）；School of Medicine, Faculty of Medicine and Health, University of Leeds（利兹大学医学与健康学院医学院）；Division of Informatics, University of Manchester（曼彻斯特大学信息学系）；Academic Unit for Ageing & Stroke Research, University of Leeds（利兹大学衰老与中风研究学术单元）；Institute of Population Health, University of Liverpool（利物浦大学人口健康研究所）
Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time
DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits
Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Hyderabad Campus（计算机科学与信息系统系，比拉理工学院，比拉理工学院，海得拉巴校区）
A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals
University of Minnesota（明尼苏达大学）；Emory University（埃默里大学）；M Health Fairview Informatics（M Health Fairview 信息学）；Indiana University（印第安纳大学）；North Memorial Health Hospital（North Memorial Health 医院）
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）
Explainable AI for Infection Prevention and Control: Modeling CPE Acquisition and Patient Outcomes in an Irish Hospital with Transformers
School of Computing（计算学院）；Dublin City University（都柏林城市大学）；ADAPT Centre（ADAPT中心）；University College Dublin（都柏林大学）；St James’s Hospital（圣詹姆斯医院）；Health Service Executive (HSE)（健康服务执行局）；Trinity College Dublin（三一学院）
Continuous Determination of Respiratory Rate in Hospitalized Patients using Machine Learning Applied to Electrocardiogram Telemetry
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Wellman Center for Photomedicine（韦尔曼光医学中心）；Healthcare Transformation Lab（医疗转型实验室）；Center for Systems Biology（系统生物学中心）
Optimization of Genomic Classifiers for Clinical Deployment: Evaluation of Bayesian Optimization to Select Predictive Models of Acute Infection and In-Hospital Mortality
