Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, 30 or 90 days, after the discharge. The motivation is to help health providers deliver better treatment and post-discharge strategies, lower the hospital readmission rate, and eventually reduce the medical costs. Due to inherent complexity of diseases and healthcare ecosystems, modeling hospital readmission is facing many challenges. By now, a variety of methods have been developed, but existing literature fails to deliver a complete picture to answer some fundamental questions, such as what are the main challenges and solutions in modeling hospital readmission; what are typical features/models used for readmission prediction; how to achieve meaningful and transparent predictions for decision making; and what are possible conflicts when deploying predictive approaches for real-world usages. In this paper, we systematically review computational models for hospital readmission prediction, and propose a taxonomy of challenges featuring four main categories: (1) data variety and complexity; (2) data imbalance, locality and privacy; (3) model interpretability; and (4) model implementation. The review summarizes methods in each category, and highlights technical solutions proposed to address the challenges. In addition, a review of datasets and resources available for hospital readmission modeling also provides firsthand materials to support researchers and practitioners to design new approaches for effective and efficient hospital readmission prediction.
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）
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）
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）
Machine Learning-Based Prediction of ICU Readmissions in Intracerebral Hemorrhage Patients: Insights from the MIMIC Databases
University of Southern California（南加州大学）；California State University, Long Beach（加州州立大学长滩分校）；California State Polytechnic University, Pomona（加州州立理工大学波莫纳分校）
Neural networks versus Logistic regression for 30 days all-cause readmission prediction
University of Zurich（苏黎世大学）；University Hospital of Zurich（苏黎世大学医院）；Yale University（耶鲁大学）；Lister Hill National Center for Biomedical Communications（利斯特希尔国家生物医学通信中心）；National Library of Medicine（美国国家医学图书馆）；Yale School of Medicine（耶鲁医学院）
Population stratification for prediction of mortality in post-AKI patients
Institute of Applied Health Sciences, University of Aberdeen（阿伯丁大学应用卫生科学学院）；Institute of Mathematics and Statistics, University of Sao Paulo（圣保罗大学数学与统计学院）
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death
Mederrata Research and Sound Prediction（Mederrata研究与声音预测公司）；NIH NIDDK（美国国立卫生研究院国家糖尿病、消化与肾脏疾病研究所）；Mederrata Research（Mederrata研究公司）；Blue Health Intelligence（蓝色健康智能公司）；Walter Reed Army Institute of Research（沃尔特·里德陆军研究所）；Sound Prediction and Mederrata Research（声音预测与Mederrata研究公司）
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）
Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries
Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia (UdeA)（热带疾病生物物理学马克斯·普朗克双人组，安蒂奥基亚大学）；Grupo de Física Atómica y Molecular, Instituto de Física, University of Antioquia (UdeA)（原子与分子物理组，物理研究所，安蒂奥基亚大学）；Graduate School and Research, Arcada University of Applied Sciences（应用科学大学研究生院）；VTT Technical Research Centre of Finland Ltd（芬兰技术研究中心）；Department of Internal Medicine, School of Medicine, University of Antioquia（内科学系，医学院，安蒂奥基亚大学）；Hospital Alma Mater de Antioquia, University of Antioquia（安蒂奥基亚大学阿尔玛 Mater 医院）
Characterizing the Value of Information in Medical Notes
University of Chicago（芝加哥大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Chicago Booth School of Business（芝加哥大学布斯商学院）；University of California, Berkeley（加州大学伯克利分校）
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）
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）
Emergency Department Optimization and Load Prediction in Hospitals
KenSci Inc（KenSci公司）；University of Washington（华盛顿大学）；Swedish Medical Center（瑞典医疗中心）；University of Washington - Bothell（华盛顿大学博塞尔分校）；University of Washington - Tacoma（华盛顿大学塔科马分校）
TabText: Language-Based Representations of Tabular Health Data for Predictive Modelling
Tandon School of Engineering（坦顿工程学院）；New York University（纽约大学）；Operations Research Center（运筹学中心）；Massachusetts Institute of Technology（麻省理工学院）；University of Wisconsin, Madison（威斯康星大学麦迪逊分校）；Foster School of Business（福斯特商学院）；University of Washington, Foster School of Business（华盛顿大学福斯特商学院）；Stern School of Business（斯特恩商学院）；Abdul Latif Jameel Clinic for Machine Learning in Health（阿卜杜勒拉赫曼·贾迈尔健康机器学习诊所）；Sloan School of Management（斯隆管理学院）
Optimization of Genomic Classifiers for Clinical Deployment: Evaluation of Bayesian Optimization to Select Predictive Models of Acute Infection and In-Hospital Mortality
