A unified machine learning approach to time series forecasting applied to demand at emergency departments
MRC Centre for Global Infectious Disease Analysis, Imperial College London（帝国理工学院MRC全球传染病分析中心）；Imperial College Healthcare NHS Trust（帝国理工学院医疗保健国民健康服务体系信托）；Faculty of Medicine, Imperial College London（帝国理工学院医学院）；Department of Infectious Disease, Imperial College London（帝国理工学院传染病系）；Department of Mathematics, Imperial College London（帝国理工学院数学系）
Michaela A. C. Vollmer, Ben Glampson, Thomas A. Mellan, Swapnil Mishra, Luca Mercuri, Ceire Costello, Robert Klaber, Graham Cooke, Seth Flaxman, Samir Bhatt
There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide adequate quality of care while maintaining standards and productivity. Managing hospital demand effectively requires an adequate knowledge of the future rate of admission. Using 8 years of electronic admissions data from two major acute care hospitals in London, we develop a novel ensemble methodology that combines the outcomes of the best performing time series and machine learning approaches in order to make highly accurate forecasts of demand, 1, 3 and 7 days in the future. Both hospitals face an average daily demand of 208 and 106 attendances respectively and experience considerable volatility around this mean. However, our approach is able to predict attendances at these emergency departments one day in advance up to a mean absolute error of +/- 14 and +/- 10 patients corresponding to a mean absolute percentage error of 6.8% and 8.6% respectively. Our analysis compares machine learning algorithms to more traditional linear models. We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. In addition to comparing and combining state-of-the-art forecasting methods to predict hospital demand, we consider two different hyperparameter tuning methods, enabling a faster deployment of our models without compromising performance. We believe our framework can readily be used to forecast a wide range of policy relevant indicators.
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）
High-dimensional point forecast combinations for emergency department demand
Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李光前医学院）；Department of Economics, Rutgers University（罗格斯大学经济学系）；Saw Swee Hock School of Public Health, National University of Singapore（新加坡国立大学 Saw Swee Hock 公共卫生学院）；Ministry of Health, Singapore（新加坡卫生部）；Tan Tock Seng Hospital, Singapore（新加坡陈笃生医院）
An Integrated Forecasting Prototype for Emergency Department Boarding Time to Support Proactive Operational Decision Making
Department of Electrical and Computer Engineering, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校电气与计算机工程系）；Department of Health Services Administration, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校卫生服务管理系）；Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校希尔斯医院医学院生物医学信息学与数据科学系）；Department of Emergency Medicine, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校急诊医学系）
Implementation and Assessment of Machine Learning Models for Forecasting Suspected Opioid Overdoses in Emergency Medical Services Data
Institute for Biomedical Informatics, University of Kentucky（肯塔基大学生物医学信息学研究所）；Substance Use Priority Research Area, University of Kentucky（肯塔基大学物质使用优先研究领域）；Dr. Bing Zhang Department of Statistics, University of Kentucky（肯塔基大学Dr. Bing Zhang统计系）；Department of Biostatistics, University of Kentucky（肯塔基大学生物统计学系）
Forecasting Faculty Placement from Patterns in Co-authorship Networks
Khoury College of Computer Sciences, Northeastern University（诺思韦普大学科里学院）；Network Science Institute, Northeastern University（网络科学研究所）；Santa Fe Institute（圣菲研究所）
Enhancing Profitability and Investor Confidence through Interpretable AI Models for Investment Decisions
School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST)（国家科技大学电气工程与计算机科学学院）；Department of Computer Science, Bahria University（巴里亚大学计算机科学系）；College of Computer and Information Sciences (CCIS), Prince Sultan University（苏丹王子大学计算机与信息科学学院）
An Empirical Study of Machine Learning Robustness and Scalability for Imbalanced Tabular Clinical Data in Emergency and Critical Care
Computer Vision Group, Institute of Cognitive Science, Osnabrück University（计算机视觉组，认知科学研究所，奥斯纳布吕克大学）；Department of Mathematics, Rhodes University（数学系，罗德斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS)（国家理论与计算科学研究所（NITheCS））
VQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical Constraints
University of Science and Technology of China（中国科学技术大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Hong Kong University of Science and Technology（香港科技大学）
Smart Metro: Deep Learning Approaches to Forecasting the MRT Line 3 Ridership
Technological University of the Philippines（菲律宾技术大学）；National University（国立大学）；Philippine Coding Camp（菲律宾编程营）；University of the Philippines Open University（菲律宾大学开放大学）
When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Université Mohammed VI Polytechnique（穆罕默德六世理工大学）；University of Oxford（牛津大学）；Imperial College London（帝国理工学院）；Rhineland-Palatinate Technical University of Kaiserslautern-Landau (RPTU)（莱茵兰-普法尔茨凯泽斯劳滕-兰道工业大学）
