Predicting patient volumes in a hospital setting is a well-studied application of time series forecasting. Existing tools usually make forecasts at the daily or weekly level to assist in planning for staffing requirements. Prompted by new COVID-related capacity constraints placed on our pediatric hospital's emergency department, we developed an hourly forecasting tool to make predictions over a 24 hour window. These forecasts would give our hospital sufficient time to be able to martial resources towards expanding capacity and augmenting staff (e.g. transforming wards or bringing in physicians on call). Using Gaussian Process Regressions (GPRs), we obtain strong performance for both point predictions (average R-squared: 82%) as well as classification accuracy when predicting the ordinal tiers of our hospital's capacity (average precision/recall: 82%/74%). Compared to traditional regression approaches, GPRs not only obtain consistently higher performance, but are also robust to the dataset shifts that have occurred throughout 2020. Hospital stakeholders are encouraged by the strength of our results, and we are currently working on moving our tool to a real-time setting with the goal of augmenting the capabilities of our healthcare workers.
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）
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）
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）
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 se…
State University of New York at Binghamton（纽约州立大学宾汉姆顿分校）
Recently, the combination of machine learning (ML) and simulation is gaining a lot of attention. This paper presents a novel application of ML within the simulation to improve patient flow within an emergency department (ED). An ML model used within a real ED simulation model to quantify the effect of detouring a patient out of the ED on the length of stay (LOS) and door-to-doctor time (DTDT) as a response to the prediction of patient admission to the hospital from the ED. The ML model trained using a set of six features including the patient age, arrival day, arrival hour of the day, and the …
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）
Over the past several years, across the globe, there has been an increase in people seeking care in emergency departments (EDs). ED resources, including nurse staffing, are strained by such increases in patient volume. Accurate forecasting of incoming patient volume in emergency departments (ED) is crucial for efficient utilization and allocation of ED resources. Working with a suburban ED in the Pacific Northwest, we developed a tool powered by machine learning models, to forecast ED arrivals and ED patient volume to assist end-users, such as ED nurses, in resource allocation. In this paper, …
Hellenic Open University（希腊开放大学）；Sismanogleio General Hospital（西斯马诺格利奥综合医院）；Thoracic Diseases General Hospital Sotiria（索提里亚胸科疾病综合医院）；National and Kapodistrian University of Athens（雅典国家与卡波迪斯特里安大学）
Introduction: One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare. Material and methods: We investigated the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, D Dimer, International Normalized Ratio, age, gender,…
Parkland Center for Clinical Innovation (PCCI)（帕克兰临床创新中心）；Parkland Community Health Plan (PCHP)（帕克兰社区健康计划）
Pediatric asthma is the most prevalent chronic childhood illness, afflicting about 6.2 million children in the United States. However, asthma could be better managed by identifying and avoiding triggers, educating about medications and proper disease management strategies. This research utilizes deep learning methodologies to predict asthma-related emergency department (ED) visit within 3 months using Medicaid claims data. We compare prediction results against traditional statistical classification model - penalized Lasso logistic regression, which we trained and have deployed since 2015. The …
Temple University（天普大学）；Lewis Katz School of Medicine, Temple University（天普大学刘易斯·卡茨医学院）
Optimization of patient throughput and wait time in emergency departments (ED) is an important task for hospital systems. For that reason, Emergency Severity Index (ESI) system for patient triage was introduced to help guide manual estimation of acuity levels, which is used by nurses to rank the patients and organize hospital resources. However, despite improvements that it brought to managing medical resources, such triage system greatly depends on nurse's subjective judgment and is thus prone to human errors. Here, we propose a novel deep model based on the word attention mechanism designed …
Development and Comparative Evaluation of Three Artificial Intelligence Models (NLP, LLM, JEPA) for Predicting Triage in Emergency Departments: A 7-Month Retrospective Proof-of-Concept
Department of Emergency Lille University Hospital（里尔大学医院急诊科）；Centre Antipoison, Lille University Hospital（里尔大学医院毒物中心）；Department of Public Health, EA 2694 & ULR 2694-METRICS（公共卫生部门，EA 2694及ULR 2694-METRICS）；Lille University（里尔大学）；Emergency Department Hôpital Pitié-Salpêtrière, AP-HP（皮蒂埃-萨尔佩特里耶医院急诊科，AP-HP）
In the United States, medical responses by fire departments over the last four decades increased by 367%. This had made it critical to decision makers in emergency response departments that existing resources are efficiently used. In this paper, we model the ambulance dispatch problem as an average-cost Markov decision process and present a policy iteration approach to find an optimal dispatch policy. We then propose an alternative formulation using post-decision states that is shown to be mathematically equivalent to the original model, but with a much smaller state space. We present a tempor…
Chemical Mass Casualty Incidents (MCI) place a heavy burden on hospital staff and resources. Machine Learning (ML) tools can provide efficient decision support to caregivers. However, ML models require large volumes of data for the most accurate results, which is typically not feasible in the chaotic nature of a chemical MCI. This study examines the application of four statistical dimension reduction techniques: Random Selection, Covariance/Variance, Pearson's Linear Correlation, and Principle Component Analysis to reduce a dataset of 311 hazardous chemicals and 79 related signs and symptoms (…
Development of a Large Language Model-based Multi-Agent Clinical Decision Support System for Korean Triage and Acuity Scale (KTAS)-Based Triage and Treatment Planning in Emergency Departments
John Bapst Memorial High School（约翰·巴普斯特纪念高中）；Minerva University（密涅瓦大学）
Validating Emergency Department Admission Predictions Based on Local Data Through MIMIC-IV
Hellenic Open University（希腊开放大学）；Sismanogleio General Hospital（西斯马诺格利奥综合医院）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚大学）；Amalia Fleming General Hospital（阿玛利亚·弗莱明综合医院）
Benchmarking emergency department triage prediction models with machine learning and large public electronic health records
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Nanyang Technological University（南洋理工大学）；Monash University（莫纳什大学）；National University of Singapore（新加坡国立大学）；Duke University（杜克大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）；Singapore General Hospital（新加坡中央医院）；Singapore Health Services（新加坡保健服务集团）
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））
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. Th…
Multi-Modal Perceiver Language Model for Outcome Prediction in Emergency Department
Qatar Computing Research Institute（卡塔尔计算研究所）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；LIMTIC Laboratory（LIMTIC实验室）；UTM University（突尼斯马纳大学）；Higher institute of computer science of Medenine（梅德宁高等计算机科学学院）；Gabes University（加贝斯大学）；Sidra Medicine（锡德拉医学中心）
Analyzing the Variations in Emergency Department Boarding and Testing the Transferability of Forecasting Models across COVID-19 Pandemic Waves in Hong Kong: Hybrid CNN-LSTM approach to quantifying building-level socioecological risk
JC School of Public Health and Primary Care, The Chinese University of Hong Kong（香港中文大学赛马会公共卫生及基层医疗学院）；Hong Kong Polytechnic University（香港理工大学）
Graz University of Technology（格拉茨工业大学）；University of Auckland（奥克兰大学）；Technical University of Darmstadt（达姆施塔特工业大学）
The performance of Emergency Departments (EDs) is of great importance for any health care system, as they serve as the entry point for many patients. However, among other factors, the variability of patient acuity levels and corresponding treatment requirements of patients visiting EDs imposes significant challenges on decision makers. Balancing waiting times of patients to be first seen by a physician with the overall length of stay over all acuity levels is crucial to maintain an acceptable level of operational performance for all patients. To address those requirements when assigning idle r…
Texas A&M University（德克萨斯农工大学）；Yale School of Medicine（耶鲁医学院）
Visual summarization of clinical data collected on patients contained within the electronic health record (EHR) may enable precise and rapid triage at the time of patient presentation to an emergency department (ED). The triage process is critical in the appropriate allocation of resources and in anticipating eventual patient disposition, typically admission to the hospital or discharge home. EHR data are high-dimensional and complex, but offer the opportunity to discover and characterize underlying data-driven patient phenotypes. These phenotypes will enable improved, personalized therapeutic…
Mednition Inc.（Mednition公司）；Emergency Nurses Association（急诊护士协会）；El Camino Hospital（埃尔卡米诺医院）；San Mateo Medical Center（圣马特奥医疗中心）；Adventist Health White Memorial（复临健康怀特纪念医院）；Ascension Health（阿森松医疗）；American Osteopathic Association（美国骨科协会）
Effective triage is critical to mitigating the effect of increased volume by accurately determining patient acuity, need for resources, and establishing effective acuity-based patient prioritization. The purpose of this retrospective study was to determine whether historical EHR data can be extracted and synthesized with clinical natural language processing (C-NLP) and the latest ML algorithms (KATE) to produce highly accurate ESI predictive models. An ML model (KATE) for the triage process was developed using 166,175 patient encounters from two participating hospitals. The model was then test…
ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance
Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）；University of California, Berkeley（加州大学伯克利分校）；University of California, San Francisco（加州大学旧金山分校）；Department of EECS, University of California, Berkeley（加州大学伯克利分校电子工程与计算机科学系）
The Ohio State University（俄亥俄州立大学）；The Abigail Wexner Research Institute at Nationwide Children’s Hospital（全国儿童医院阿比盖尔·韦克斯纳研究所）
Trauma mortality results from a multitude of non-linear dependent risk factors including patient demographics, injury characteristics, medical care provided, and characteristics of medical facilities; yet traditional approach attempted to capture these relationships using rigid regression models. We hypothesized that a transfer learning based machine learning algorithm could deeply understand a trauma patient's condition and accurately identify individuals at high risk for mortality without relying on restrictive regression model criteria. Anonymous patient visit data were obtained from years …
Effective Predictive Modeling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting
Canberra Health Services, Canberra Hospital（堪培拉卫生服务局，堪培拉医院）；University of Technology Sydney（悉尼科技大学）；University of New South Wales（新南威尔士大学）；The Australian National University（澳大利亚国立大学）；Obuda University（欧布达大学）
Detection of sepsis during emergency department triage using machine learning
Mednition Inc.（麦迪逊公司）；Sutter Mills-Peninsula Medical Center（萨特米尔半岛医疗中心）；Ascension Health（阿森松医疗）；Adventist Health White Memorial（基督复临安息日会怀特纪念医疗中心）；San Mateo Medical Center（圣马特奥医疗中心）；El Camino Hospital（埃尔卡米诺医院）；Emergency Nurses Association（急诊护士协会）；Duke University（杜克大学）
We present a data-driven optimization framework for redesigning police patrol zones in an urban environment. The objectives are to rebalance police workload among geographical areas and to reduce response time to emergency calls. We develop a stochastic model for police emergency response by integrating multiple data sources, including police incidents reports, demographic surveys, and traffic data. Using this stochastic model, we optimize zone redesign plans using mixed-integer linear programming. Our proposed design was implemented by the Atlanta Police Department in March 2019. By analyzing…
Quantifying the Impact of Ambulance Ramping: A Multi-Year Analysis of Victorian Emergency Medical Services Cases
The Centre of Excellence in Paramedicine (CoEP), Victoria University（卓越急救医学中心（CoEP），维多利亚大学）；Center for Research and Evaluation Ambulance Victoria（维多利亚救护车研究与评估中心）
University of Oxford（牛津大学）；Oxford University Hospitals Trust（牛津大学医院信托）
Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing with patients who come from the emergency department. In this work we propose a student-teacher network via reinforcement learning to deal with this specific problem. A representation of the weights of the student network is treated as the state and is fed as an input to the teacher network. The teacher network's action is to select the most appropriate batch of data to train the student network on from a training set s…
