Developing and validating multi-modal models for mortality prediction in COVID-19 patients: a multi-center retrospective study
Stanford University（斯坦福大学）；Massachusetts Institute of Technology（麻省理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Fundacion Progreso y Salud（进步与健康基金会）；National Tsing Hua University（台湾清华大学）；Albert Einstein Medical Center（阿尔伯特·爱因斯坦医疗中心）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；National University Hospital（新加坡国立大学医院）；Hoboken University Medical Center–CarePoint Health（霍博肯大学医疗中心-CarePoint Health）；Hospital Universitario Monteprincipe（蒙特普林西比大学医院）；Grupo HM Hospitales（HM医院集团）；Seoul National University College of Medicine（首尔大学医学院）；Centro Nacional de Investigaciones Cardiovasculares（国家心血管研究中心）；Instituto de Salud Carlos III（卡洛斯三世健康研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）
Joy Tzung-yu Wu, Miguel Ángel Armengol de la Hoz, Po-Chih Kuo, Joseph Alexander Paguio, Jasper Seth Yao, Edward Christopher Dee, Wesley Yeung, Jerry Jurado, Ach… 展开作者
Joy Tzung-yu Wu, Miguel Ángel Armengol de la Hoz, Po-Chih Kuo, Joseph Alexander Paguio, Jasper Seth Yao, Edward Christopher Dee, Wesley Yeung, Jerry Jurado, Achintya Moulick, Carmelo Milazzo, Paloma Peinado, Paula Villares, Antonio Cubillo, José Felipe Varona, Hyung-Chul Lee, Alberto Estirado, José Maria Castellano, Leo Anthony Celi
The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N=2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N=242) and an academic center in Seoul, Republic of Korea (N=336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day-mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification and/or optimization.
Stony Brook University（石溪大学）；Newark Beth Israel Medical Center（纽瓦克贝丝以色列医疗中心）；University Hospitals Cleveland Medical Center（大学医院克利夫兰医疗中心）
We predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. DL and machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using patient CXRs. A novel radiomic embedding framework was also explored for outcome prediction. All …
Pulmonary Embolism Mortality Prediction Using Multimodal Learning Based on Computed Tomography Angiography and Clinical Data
Rhode Island Hospital（罗德岛医院）；Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Xidian University（西安电子科技大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；University of Michigan（密歇根大学）
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore Health Services（新加坡保健服务集团）；Singapore General Hospital（新加坡中央医院）；National University of Singapore（新加坡国立大学）；Duke University（杜克大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）
Background: Elderly patients with MODS have high risk of death and poor prognosis. The performance of current scoring systems assessing the severity of MODS and its mortality remains unsatisfactory. This study aims to develop an interpretable and generalizable model for early mortality prediction in elderly patients with MODS. Methods: The MIMIC-III, eICU-CRD and PLAGH-S databases were employed for model generation and evaluation. We used the eXtreme Gradient Boosting model with the SHapley Additive exPlanations method to conduct early and interpretable predictions of patients' hospital outcom…
Imperial College London（伦敦帝国理工学院）；National University of Singapore（新加坡国立大学）；University of Copenhagen（哥本哈根大学）
The COVID-19 pandemic has caused over 6.4 million registered deaths to date and has had a profound impact on economic activity. Here, we study the interaction of transmission, mortality, and the economy during the SARS-CoV-2 pandemic from January 2020 to December 2022 across 25 European countries. We adopt a Bayesian Mixed Effects model with auto-regressive terms. We find that increases in disease transmission intensity decreases Gross domestic product (GDP) and increases daily excess deaths, with a longer lasting impact on excess deaths in comparison to GDP, which recovers more rapidly. Broad…
PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT and Radiology Report Dataset for Medical Imaging Research
PET Center, Huashan Hospital, Fudan University（复旦大学附属华山医院PET中心）；Shanghai Academy of Artificial Intelligence for Science（上海科学智能研究院）；Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；Shanghai Universal Medical Imaging Diagnostic Center（上海环球医学影像诊断中心）；Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；School of Data Science, Fudan University（复旦大学数据科学学院）
Phu Tho Department of Health（富寿省卫生厅）；Vingroup Big Data Institute（Vingroup大数据研究院）；VinUniversity（VinUniversity大学）；Phu Tho General Hospital（富寿综合医院）
Computer-Aided Diagnosis (CAD) systems for chest radiographs using artificial intelligence (AI) have recently shown a great potential as a second opinion for radiologists. The performances of such systems, however, were mostly evaluated on a fixed dataset in a retrospective manner and, thus, far from the real performances in clinical practice. In this work, we demonstrate a mechanism for validating an AI-based system for detecting abnormalities on X-ray scans, VinDr-CXR, at the Phu Tho General Hospital - a provincial hospital in the North of Vietnam. The AI system was directly integrated into …
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）
Qatar University（卡塔尔大学）；University of Dhaka（达卡大学）；College of Medicine, QU Health, Qatar University（卡塔尔大学QU Health医学院）；Universiti Kebangsaan Malaysia（马来西亚国民大学）
COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics, and patient outcomes were investigated using machine learning tools to identify key biomarkers to p…
Universitat Politècnica de València（瓦伦西亚理工大学）；Instituto de Investigación Sanitaria La Fe（拉费健康研究所）；Universitat de València（瓦伦西亚大学）；Consejo Superior de Investigaciones Científicas（西班牙国家研究委员会）
Background: Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to guarantee a minimum level of quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of risk of one-year mortality. Objectives: The main objective of this work is to develop and validate machine-learning based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Methods: Five machine learning techniques were applied in our study to develop machine-learning predictive mo…
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
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）
Northeastern University（东北大学）；New York University School of Medicine（纽约大学医学院）
Patients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on a deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critical ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice. Basically, we modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process. Based on individual …
NYU Abu Dhabi（纽约大学阿布扎比分校）；Abu Dhabi Health Services（阿布扎比医疗服务公司）；G42 Healthcare（G42医疗）
Existing prognostic tools mainly focus on predicting the risk of mortality among patients with coronavirus disease 2019. However, clinical evidence suggests that COVID-19 can result in non-mortal complications that affect patient prognosis. To support patient risk stratification, we aimed to develop a prognostic system that predicts complications common to COVID-19. In this retrospective study, we used data collected from 3,352 COVID-19 patient encounters admitted to 18 facilities between April 1 and April 30, 2020, in Abu Dhabi (AD), UAE. The hospitals were split based on geographical proximi…
The Ohio State University（俄亥俄州立大学）；The Abigail Wexner Research Institute at Nationwide Children’s Hospital（全国儿童医院阿比盖尔·韦克斯纳研究所）
Trauma mortality results from a multitude of non-linear dependent risk factors including patient demographics, injury characteristics, medical care provided, and characteristics of medical facilities; yet traditional approach attempted to capture these relationships using rigid regression models. We hypothesized that a transfer learning based machine learning algorithm could deeply understand a trauma patient's condition and accurately identify individuals at high risk for mortality without relying on restrictive regression model criteria. Anonymous patient visit data were obtained from years …
Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI
Department of Radiology, West China Second University Hospital, Sichuan University（四川大学华西第二医院放射科）；School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University（清华大学医学院生物医学工程系）；Department of Radiology, Sichuan Provincial Woman’s and Children’s Hospital, The Affiliated Women’s and Children’s Hospital of Chengdu Medical College（四川省妇幼保健院放射科，成都医学院附属妇幼医院）；Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China（成都妇女儿童中央医院，电子科技大学医学院）；Department of Radiology, The Third Affiliated Hospital of Zhengzhou University（郑州大学第三附属医院放射科）；Qujing Maternal and Child Health Hospital, Qujing, China（曲靖 maternal and child health hospital, Qujing, China）
Fondazione Bruno Kessler Research Institute（布鲁诺·凯斯勒基金会研究院）；Massachusetts Institute of Technology（麻省理工学院）；National University Hospital（新加坡国立大学医院）；University of Southampton（南安普顿大学）；University of Virginia School of Medicine（弗吉尼亚大学医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）
Purpose. Elevations in initially obtained serum lactate levels are strong predictors of mortality in critically ill patients. Identifying patients whose serum lactate levels are more likely to increase can alert physicians to intensify care and guide them in the frequency of tending the blood test. We investigate whether machine learning models can predict subsequent serum lactate changes. Methods. We investigated serum lactate change prediction using the MIMIC-III and eICU-CRD datasets in internal as well as external validation of the eICU cohort on the MIMIC-III cohort. Three subgroups were …
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）
Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study
Harold and Marcus Inge Department of Industrial and Manufacturing Engineering, The Pennsylvania State University（宾夕法尼亚州立大学哈罗德和马库斯·英奇工业与制造工程系）；Penn State College of Medicine, The Pennsylvania State University（宾夕法尼亚州立大学医学院）；Department of Gastroenterology, SRM Institutes for Medical Science（SRM医学科学研究所胃肠病学系）
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）
Temporal Fusion Nexus: A task-agnostic multi-modal embedding model for clinical narratives and irregular time series in post-kidney transplant care
Hahn-Schickard（哈恩-施克特德研究所）；University of Freiburg（弗赖堡大学）；Charité University Medical Center（查理医院大学医学中心）；Hasso Plattner Institute for Digital Engineering, University of Potsdam（哈索·platner数字工程研究所，波茨坦大学）；DFKI（德意志联邦防务研究院）；University Hospital Essen（埃森大学医院）；University Hospital Erlangen（埃尔兰根大学医院）
Computer Technology Associates（计算机技术协会）；Imedacs（伊梅达克斯公司）；Children’s National Health System（儿童国家医疗系统）；NYU Langone Health（纽约大学朗格尼医学中心）；MedStar Health System（梅德斯塔医疗系统）
Background: While machine learning (ML) models are rapidly emerging as promising screening tools in critical care medicine, the identification of homogeneous subphenotypes within populations with heterogeneous conditions such as pediatric sepsis may facilitate attainment of high-predictive performance of these prognostic algorithms. This study is aimed to identify subphenotypes of pediatric sepsis and demonstrate the potential value of partitioned data/subtyping-based training. Methods: This was a retrospective study of clinical data extracted from medical records of 6,446 pediatric patients t…
Acute kidney injury (AKI) is a common and serious complication after a surgery which is associated with morbidity and mortality. The majority of existing perioperative AKI risk score prediction models are limited in their generalizability and do not fully utilize the physiological intraoperative time-series data. Thus, there is a need for intelligent, accurate, and robust systems, able to leverage information from large-scale data to predict patient's risk of developing postoperative AKI. A retrospective single-center cohort of 2,911 adult patients who underwent surgery at the University of Fl…
Pitfalls of Administrative Censoring in Survival Models with Time-Indexed Inputs
Center for Data Science, New York University（纽约大学数据科学中心）；University of Chicago（芝加哥大学）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Ataraxis AI（Ataraxis人工智能公司）
Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation
Peking University（北京大学）；National University of Singapore（新加坡国立大学）；Peking University First Hospital（北京大学第一医院）；Chinese Academy of Medical Sciences（中国医学科学院）；Ningbo Yinzhou No.2 Hospital（宁波市鄞州第二医院）；Yinzhou District Center for Disease Control and Prevention（鄞州区疾病预防控制中心）
Pulmonologists-Level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach
University of Southern Denmark（南丹麦大学）；Vejle Hospital, University Hospital of Southern Denmark（南丹麦大学附属瓦埃勒医院）；Institute of Regional Health Research, University of Southern Denmark（南丹麦大学区域健康研究所）
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）
Effective triage is critical to mitigating the effect of increased volume by accurately determining patient acuity, need for resources, and establishing effective acuity-based patient prioritization. The purpose of this retrospective study was to determine whether historical EHR data can be extracted and synthesized with clinical natural language processing (C-NLP) and the latest ML algorithms (KATE) to produce highly accurate ESI predictive models. An ML model (KATE) for the triage process was developed using 166,175 patient encounters from two participating hospitals. The model was then test…
Explainable Machine Learning for ICU Readmission Prediction
School of Chemistry and Molecular Biosciences, The University of Queensland（昆士兰大学化学与分子生物科学学院）；Systems and Computational Biology, Bio21 Institute, The University of Melbourne（墨尔本大学生物21研究所系统与计算生物学系）；Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute（贝克心脏与糖尿病研究所计算生物学与临床信息学系）；Baker Department of Cardiometabolic Health, The University of Melbourne（墨尔本大学贝克代谢健康系）；Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne（墨尔本大学医学、牙医学与健康科学学院）；The Department of Surgery, St. Vincent’s Hospital Melbourne（墨尔本圣文森特医院外科）；Centre for the Digital Transformation of Health, School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院健康数字化转型中心）；The Department of Anaesthesia and Perioperative Medicine, Monash Health（莫纳什医疗中心麻醉与围手术医学科）；The Intensive Care Unit at Peninsula Private Hospital, Ramsay Healthcare（拉姆齐医疗半岛私立医院重症监护室）；The Department of Intensive Care Medicine, The Alfred Hospital（阿尔弗雷德医院重症医学科）；The Department of Anaesthesiology and Perioperative Medicine, The Alfred Hospital（阿尔弗雷德医院麻醉与围手术医学科）
