Identification, explanation and clinical evaluation of hospital patient subtypes
University of Bristol（布里斯托大学）；University Hospitals Bristol NHS Foundation Trust（布里斯托大学医院NHS基金会信托）
Enrico Werner, Jeffrey N. Clark, Ranjeet S. Bhamber, Michael Ambler, Christopher P. Bourdeaux, Alexander Hepburn, Christopher J. McWilliams, Raul Santos-Rodriguez
We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning. In parallel, clinicians assessed intra-cluster similarities and inter-cluster differences of the identified patient subtypes within the context of their clinical knowledge. By confronting the outputs of both automatic and clinician-based explanations, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.
GWO-FI: A novel machine learning framework by combining Gray Wolf Optimizer and Frequent Itemsets to diagnose and investigate effective factors on In-Hospital Mortality and Length of Stay among Kermanshahian Cardiovascular Disease patients
Cardiovascular Research Center, Health Research Institute, Imam Ali Hospital, Kermanshah University of Medical Sciences（克尔曼沙赫医科大学伊玛目阿里医院心血管研究中心、健康研究所）
Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model
Georgia Institute of Technology（佐治亚理工学院）；University of Miami（迈阿密大学）；University of California（加利福尼亚大学）；Boston University（波士顿大学）；University of Pennsylvania（宾夕法尼亚大学）
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）
Identifying Dementia Subtypes with Electronic Health Records
Department of Computer Science and Engineering, Washington University in St. Louis（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（信息学院，华盛顿大学医学院）；Department of Neurology, Washington University School of Medicine（神经病学系，华盛顿大学医学院）；Institute for Clarity in Documentation（文档清晰研究所）；Inria Paris-Rocquencourt（巴黎-罗克奎恩特研究所）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation
Bioinformatics Center, Kyoto University（京都大学生物信息学中心）；SANKEN, Osaka University（大阪大学SANKEN（大阪大学蛋白质研究所））；National Institute of Advanced Industrial Science and Technology (AIST)（日本国立先进工业科学技术研究所）；RIKEN Center for Computational Science（理化学研究所计算科学中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
College of Computer Science, Sichuan University（四川大学计算机学院）；Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences（中国科学院新疆物理化学研究所）；College of Mathematics, Sichuan University（四川大学数学学院）；School of Artificial Intelligence and Data Science, University of International Business and Economics（国际经贸大学人工智能与数据科学学院）
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）
UnPaSt: unsupervised patient stratification by biclustering of omics data
University of Hamburg（汉堡大学）；Technical University of Munich（慕尼黑工业大学）；Netherlands Cancer Institute（荷兰癌症研究所）；ACMetric；University of Campinas（坎皮纳斯大学）；University of British Columbia（不列颠哥伦比亚大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大大学埃尔兰根-纽伦堡）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Altius Institute for Biomedical Sciences（Altius生物医学科学研究所）；University of Liège（列日大学）；Aarhus University（奥胡斯大学）；Aarhus University Hospital（奥胡斯大学医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Simon Fraser University（西蒙弗雷泽大学）；Vancouver Prostate Centre（温哥华前列腺中心）；Leibniz Institute of Virology（莱布尼茨病毒学研究所）；University of Southern Denmark（南丹麦大学）
Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records
Academy of Arts and Design, Tsinghua University（清华大学美术学院）；Massachusetts Institute of Technology（麻省理工学院）
Instance Generation for Patient-to-room Assignment and Admission Scheduling Based on Real Hospital Data
Chair of Combinatorial Optimization, RWTH Aachen University（组合优化研究所，亚琛工业大学）；Chair of Algorithms and Complexity, RWTH Aachen University（算法与复杂性研究所，亚琛工业大学）
Predicting the Stay Length of Patients in Hospitals using Convolutional Gated Recurrent Deep Learning Model
Canberra Health Services（堪培拉卫生服务局）；Canberra Hospital（堪培拉医院）；Torrens University Australia（澳大利亚托伦斯大学）；The Australian National University（澳大利亚国立大学）；Australian National University（澳大利亚国立大学）
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）
An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records
Department of Electronics, Information and Bioengineering（电子、信息与生物工程系）；Politecnico di Milano（米兰理工大学）；Lero – the Science Foundation Ireland Research Centre for Software（爱尔兰科学基金会软件研究中心）；University of Limerick（利默ick大学）
Enhancing Uncertain Demand Prediction in Hospitals Using Simple and Advanced Machine Learning
School of Mathematics, University of Bristol（布里斯托大学数学学院）；School of Engineering and Informatics, University of Sussex（萨塞克斯大学工程与信息学院）；NHS Integrated Care Board（英国国家医疗服务体系综合护理委员会）；School of Business, University of Bristol（布里斯托大学商学院）；Centre Hospitalier Universitaire Vaudois (CHUV)（沃州大学医院中心）
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）
An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients
ARTORG Center for Biomedical Engineering Research, University of Bern（伯尔尼大学生物医学工程研究ARTORG中心）；University Hospital of Diabetology, Endocrinology, Nutritional Medicine and Metabolism (UDEM) Inselspital, University Hospital（糖尿病、内分泌、营养医学与代谢大学医院（UDEM）因塞尔医院）
