Patient length of stay (LoS) is a critical metric for evaluating the efficacy of hospital management. The primary objectives encompass to improve efficiency and reduce costs while enhancing patient outcomes and hospital capacity within the patient journey. By seamlessly merging data-driven techniques with simulation methodologies, the study proposes an all-encompassing framework for the optimization of patient flow. Using a comprehensive dataset of 2.3 million de-identified patient records, we analyzed demographics, diagnoses, treatments, services, costs, and charges with machine learning models (Decision Tree, Logistic Regression, Random Forest, Adaboost, LightGBM) and Python tools (Spark, AWS clusters, dimensionality reduction). Our model predicts patient length of stay (LoS) upon admission using supervised learning algorithms. This hybrid approach enables the identification of key factors influencing LoS, offering a robust framework for hospitals to streamline patient flow and resource utilization. The research focuses on patient flow, corroborating the efficacy of the approach, illustrating decreased patient length of stay within a real healthcare environment. The findings underscore the potential of hybrid data-driven models in transforming hospital management practices. This innovative methodology provides generally flexible decision-making, training, and patient flow enhancement; such a system could have huge implications for healthcare administration and overall satisfaction with healthcare.
Continual Prediction from EHR Data for Inpatient Acute Kidney Injury
University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校）；Aurora Research Institute（奥罗拉研究所）；Aurora Health Care（奥罗拉医疗保健）；Milwaukee Kidney Associates（密尔沃基肾脏协会）；Allina Health System（阿利纳医疗系统）
Sum of previous inpatient serum creatinine measurements predicts acute kidney injury in rehospitalized patients
University of Rochester School of Medicine and Dentistry（罗切斯特大学医学与牙科学院）；University of Rochester Medical Center（罗切斯特大学医学中心）；Rochester Center for Health Informatics（罗切斯特健康信息学中心）；University of Rochester（罗切斯特大学）；University of Rochester Information Systems Division（罗切斯特大学信息系统部）
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）
From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU
Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Berlin Institute of Health at Charité – Universitätsmedizin Berlin（柏林夏里特医学院柏林健康研究所）；Universitätsmedizin Greifswald（格赖夫斯瓦尔德大学医学院）；Birmingham City University（伯明翰城市大学）
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）
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）
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）
Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network
University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；McGovern Medical School of The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦戈文医学院）
Mining the contribution of intensive care clinical course to outcome after traumatic brain injury
University of Cambridge（剑桥大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Humanitas University（Humanitas大学）；Karolinska Institutet（卡罗林斯卡学院）；University of Stirling（斯特灵大学）；Leiden University Medical Center（莱顿大学医学中心）；Cambridge Centre for Artificial Intelligence in Medicine（剑桥医学人工智能中心）
