Hospital readmission prediction is critical for clinical decision support, aiming to identify patients at risk of returning within 30 days post-discharge. High readmission rates often indicate inadequate treatment or post-discharge care, making effective prediction models essential for optimizing resources and improving patient outcomes. We propose PT, a Transformer-based model that integrates Electronic Health Records (EHR), medical images, and clinical notes to predict 30-day all-cause hospital readmissions. PT extracts features from raw data and uses specialized Transformer blocks tailored to the data's complexity. Enhanced with Random Forest for EHR feature selection and test-time ensemble techniques, PT achieves superior accuracy, scalability, and robustness. It performs well even when temporal information is missing. Our main contributions are: (1)Simplicity: A powerful and efficient baseline model outperforming existing ones in prediction accuracy; (2)Scalability: Flexible handling of various features from different modalities, achieving high performance with just clinical notes or EHR data; (3)Robustness: Strong predictive performance even with missing or unclear temporal data.
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）
Predicting Risk-of-Readmission for Congestive Heart Failure Patients: A Multi-Layer Approach
University of Washington Tacoma（塔科马华盛顿大学）；Ghent University（根特大学）；Multicare Health System（多凯尔医疗系统）
降低充血性心力衰竭（CHF）患者在出院后30天内再入院的风险非常重要，因为这种再入院不仅费用昂贵，而且是提供者护理和治疗质量的关键指标。准确预测再入院的风险可以使医院识别高风险患者，并最终通过识别在许多情况下导致这种再入院的因素来提高护理质量。在本文中，我们研究的问题，预测风险的再入院作为一个监督学习问题，使用多层分类方法。与我们的方法相反，早期的文献没有充分地试图通过建立直接预测模型来评估30天再入院的风险值。我们首先将问题分为不同的阶段，（a）一般风险（b）60天内的风险（c）30天内的风险，然后为每个阶段构建合适的分类器，从而提高使用多层决策准确预测风险的能力。我们的方法的优点是，我们可以使用不同的分类模型的子任务，更适合各自的问题。此外，与一次性单层方法相比，可以使用不同的特征和训练数据来解决每个子任务，从而导致高度自信的诊断或风险。对Multicare Health Systems的实际医院患者记录数据的实验评估表明，与之前的尝试相比，我们的模型在预测CHF患者出院后30天内的再入院风险方面明显更好。
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）
Simple Recurrent Neural Networks is all we need for clinical events predictions using EHR data
School of Biomedical Informatics, University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心生物医学信息学院）；School of Public Health, University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心公共卫生学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Computer Science Department, School of Engineering, Rice University（莱斯大学工程学院计算机科学系）
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）
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）
Generalist Foundation Models Are Not Clinical Enough for Hospital Operations
Courant Institute School of Mathematics, Computing, and Data Science（Courant学院数学、计算与数据科学学院）；New York University（纽约大学）；Department of Neurosurgery（神经外科部）；Global AI Frontier Lab（全球人工智能前沿实验室）；Electrical and Computer Engineering（电气与计算机工程）；Tandon School of Engineering（Tandon工程学院）；Grossman School of Medicine（Grossman医学院）；Department of Medicine（医学部）；Department of Computer Science（计算机科学部）；Department of Surgery（外科部）；Division of Applied AI Technologies（应用人工智能技术部）；Department of Population Health（人口健康部）；ETH Zurich（苏黎世联邦理工学院）
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations
Stanford Center for Biomedical Informatics Research（斯坦福生物医学信息学研究中心）；Stanford University（斯坦福大学）；University of Toronto（多伦多大学）；Massachusetts Institute of Technology（麻省理工学院）；Institute for Medical and Evaluative Sciences（医学与评估科学研究所）
