Hospital length of stay (LOS) is one of the most essential healthcare metrics that reflects the hospital quality of service and helps improve hospital scheduling and management. LOS prediction helps in cost management because patients who remain in hospitals usually do so in hospital units where resources are severely limited. In this study, we reviewed papers on LOS prediction using machine learning and statistical approaches. Our literature review considers research studies that focus on LOS prediction for stroke patients. Some of the surveyed studies revealed that authors reached contradicting conclusions. For example, the age of the patient was considered an important predictor of LOS for stroke patients in some studies, while other studies concluded that age was not a significant factor. Therefore, additional research is required in this domain to further understand the predictors of LOS for stroke patients.
Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals
Stanford University（斯坦福大学）；University of California, San Francisco（加州大学旧金山分校）；Emory University（埃默里大学）；Sungkyunkwan University（成均馆大学）；Jeju National University Hospital（济州国立大学医院）
A predictive analytics approach for stroke prediction using machine learning and neural networks
ADAPT SFI Research Centre（ADAPT科学基金会爱尔兰研究中心）；University College Dublin（都柏林大学）；Beijing University of Technology（北京工业大学）；Beijing-Dublin International College（北京都柏林国际学院）；National College of Ireland（爱尔兰国家学院）；National University of Singapore（新加坡国立大学）
Universidade da Coruña（拉科鲁尼亚大学）；Instituto de Investigación Biomédica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所）；Complexo Hospitalario Universitario de A Coruña (CHUAC)（拉科鲁尼亚大学医院综合体）；Health Research Institute of Santiago de Compostela (IDIS)（圣地亚哥-德孔波斯特拉健康研究所）
Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation
New Mexico Institute of Mining and Technology（新墨西哥矿业技术学院）；Oak Ridge National Laboratory（橡树岭国家实验室）；The University of Tennessee Health Science Center（田纳西大学健康科学中心）；Illinois State University（伊利诺伊州立大学）
CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients
School of Artificial Intelligence, Chongqing University of Technology（重庆理工大学人工智能学院）；Chongqing Key Laboratory of Embodied Intelligence Perception and Autonomous Learning for Humanoid Robots（重庆市人形机器人感知与自主学习重点实验室）；Key Laboratory of Advanced Equipment Intelligence of the Chongqing Education Commission（重庆市教育委员会先进设备智能重点实验室）；School of Smart Health, Chongqing Polytechnic University of Electronic Technology（重庆理工大学电子工程学院智能健康学院）；Department of Language Science and Technology, The Hong Kong Polytechnic University（香港理工大学语言科学与技术系）；School of Pharmacy and Bioengineering, Chongqing University of Technology（重庆理工大学药学院与生物工程学院）
IFRA: a machine learning-based Instrumented Fall Risk Assessment Scale derived from Instrumented Timed Up and Go test in stroke patients
Teseo Srl（特塞奥有限公司）；University of Genoa（热那亚大学）；CantoniLab（坎托尼实验室）；Need Institute, Foundation for the Cure and Rehabilitation of Neurological Diseases（神经疾病治疗与康复基金会尼德研究所）；Casa Di Cura Igea（伊 gea 疗养医院）
Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation
The University of Western Australia（西澳大利亚大学）；Harry Perkins Institute of Medical Research（哈里·珀金斯医学研究所）；Sir Charles Gairdner Hospital（查尔斯·盖尔德纳爵士医院）；Murdoch University（默多克大学）；University of Ottawa Heart Institute（渥太华大学心脏研究所）；Fiona Stanley Hospital（菲奥娜·斯坦利医院）；University of Ottawa（渥太华大学）；Curtin University（科廷大学）
Designing Personalized Interaction of a Socially Assistive Robot for Stroke Rehabilitation Therapy
Carnegie Mellon University（卡内基梅隆大学）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等理工学院）；Madeira Interactive Technology Institute, University of Madeira（马德拉大学马德拉交互技术研究所）
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Department of Industrial and Systems Engineering, University of Southern California（1 工业与系统工程系，南加州大学）；Colorectal Research Center, Iran University of Medical Sciences（2 肠道癌研究中心，伊朗医学科学大学）；Department of Health Science, California State University, Long Beach (CSULB)（3 健康科学系，长滩加州州立大学（CSULB））；Department of Industrial and Manufacturing Engineering, California State Polytechnic University, Pomona（4 工业与制造工程系，庞纳加州州立理工大学）
Integrating uncertainty in deep neural networks for MRI based stroke analysis
University of Zurich (UZH)（苏黎世大学）；Zurich University of Applied Sciences (ZHAW)（苏黎世应用科技大学）；Konstanz University of Applied Sciences（康斯坦茨应用科技大学）；University Hospital Zurich (UHZ)（苏黎世大学医院）
Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers
Pázmány Péter Catholic University Faculty of Information Technology（帕兹曼尼·彼得天主教大学信息技术学院）；Faculty of Biomedical Engineering, Technion Institute of Technology（以色列理工学院生物医学工程学院）；Ingham Institute for Applied Medical Research, Sydney Brain Center UNSW（新南威尔士大学悉尼大脑中心英厄姆应用医学研究所）
CERN（欧洲核子研究中心）；University of L'Aquila（拉奎拉大学）；University of Trieste（的里雅斯特大学）；University of Genoa（热那亚大学）；Sapienza University（罗马大学）；Policlinico Universitario Agostino Gemelli（阿戈斯蒂诺·杰梅利大学综合医院）
Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
WeBank Co., Ltd.（微众银行股份有限公司）；Tencent Jarvis Lab（腾讯天衍实验室）；Tsinghua-Berkeley Shenzhen Institute, Tsinghua University（清华大学清华-伯克利深圳学院）；Lehigh University（理海大学）；Hong Kong University of Science and Technology（香港科技大学）；Southern University of Science and Technology（南方科技大学）；Nanyang Technological University（南洋理工大学）
Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation
Thurgau Institute for Digital Transformation (TIDIT)（图尔豪数字转型研究所）；HTWG Konstanz – University of Applied Sciences Konstanz（康斯坦茨应用科学大学）；Department of Neurology, University Hospital Zurich (USZ)（苏黎世大学神经科部）；University of Zurich (UZH)（苏黎世大学）
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）
Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning
Queens College of the City University of NY（纽约市立大学皇后学院）；National Chung Cheng University（台湾中正大学）；Ditmanson Medical Foundation Chia-Yi Christian Hospital（戴德森医疗财团法人嘉义基督教医院）
Cryptogenic stroke and migraine: using probabilistic independence and machine learning to uncover latent sources of disease from the electronic health record
Vanderbilt University School of Medicine（范德比尔特大学医学院）；Vanderbilt University Medical Center（范德比尔特大学医学中心）
Confounding Masquerading as Improvement: A Systematic Evaluation of Offline Reinforcement Learning for Stroke Antithrombotic Treatment in a 129,000-Patient Registry
Multi-objective optimization and explanation for stroke risk assessment in Shanxi province
Beijing Normal University at Zhuhai（北京师范大学珠海校区）；Beijing Normal University（北京师范大学）；Duke Kunshan University（昆山杜克大学）；York University（约克大学）；Shanxi Provincial People’s Hospital（山西省人民医院）
中风是中国的主要死亡原因（Zhou et al. The Lancet 2019）。以山西省的数据集为例，通过SHAP DeepExplainer软件识别出每个患者在低/中/高/发作四种状态下的风险，并提供状态转换趋势。为了提高不平衡样本集上的精度，通过灵活选择和添加二次交互特征，提出了二次交互深度神经网络（QIDNN）模型。实验结果表明，具有7个交互特征的QIDNN模型达到了$83.25\%$的最高精度。血压、缺乏运动、吸烟、体重和总胆固醇是最重要的五个特征。然后，为了在最紧急的状态，攻击状态的高召回率，中风的发生预测作为一个辅助目标，受益于多目标优化。与相同特征的QIDNN（从$67.93\%$）相比，$24.9\%$（到$84.83\%$）在提高预测准确率的同时，也提高了攻击状态的召回率。本文的预测模型和分析工具不仅给出了理论上的优化预测方法，而且提供了每个患者的风险状态和转移方向的归因解释，为医生分析和诊断疾病提供了有利的工具。
Comparing Baseline and Day-1 Diffusion MRI Using Multimodal Deep Embeddings for Stroke Outcome Prediction
1 Department of Computer Science, University of Geneva, Switzerland 2 Department of Electrical \& Computer Engineering, University of Florida, FL, USA 3 Service of Medical Informatics, University Hospital of Geneva, Switzerland 4 Department of Imaging；Medical Informatics, University of Geneva, Switzerland
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）
Machine Learning Methods for Identifying Atrial Fibrillation Cases and Their Predictors in Patients With Hypertrophic Cardiomyopathy: The HCM-AF-Risk Model
University of Delaware（特拉华大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Taipei Veterans General Hospital（台北荣民总医院）；National Yang-Ming University（阳明大学）；University of California San Francisco（加利福尼亚大学旧金山分校）
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）
