AIMC Topic: Crowding

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Predicting patient risk of leaving without being seen using machine learning: a retrospective study in a single overcrowded emergency department.

BMC emergency medicine Jul 15, 2025
Emergency department (ED) overcrowding has become a critical issue in hospital management, leading to increased patient wait times and higher rates of individuals leaving without being seen (LWBS). This study aims to identify key factors influencing ...
Risk Assessment Retrospective Studies Adolescent Triage Female Male Middle Aged Young Adult Waiting Lists Adult No-Show Patients Aged Crowding Emergency Service, Hospital Humans Machine Learning Italy
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Towards real-world monitoring scenarios: An improved point prediction method for crowd counting based on contrastive learning.

PloS one Jul 2, 2025
In open environments, complex and variable backgrounds and dense multi-scale targets are two key challenges for crowd counting. Due to the reliance on supervised learning with labeled data, current methods struggle to adapt to crowd detection in comp...
Humans Neural Networks, Computer Machine Learning Algorithms Crowding
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Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.

BMC emergency medicine Jan 6, 2025
INTRODUCTION: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the...
Emergency Service, Hospital France Male Middle Aged Patient Admission Crowding Humans Machine Learning Retrospective Studies Female Algorithms
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Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.

BMC medical informatics and decision making Dec 18, 2024
BACKGROUND: Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of cal...
Emergency Service, Hospital Crowding Humans Machine Learning Forecasting
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Optimized deep maxout for crowd anomaly detection: A hybrid optimization-based model.

Network (Bristol, England) Sep 20, 2024
Monitoring Surveillance video is really time-consuming, and the complexity of typical crowd behaviour in crowded situations makes this even more challenging. This has sparked a curiosity about computer vision-based anomaly detection. This study intro...
Algorithms Humans Deep Learning Video Recording Crowding
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Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel.

Health care management science Jun 10, 2024
This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acu...
Neural Networks, Computer Waiting Lists Efficiency, Organizational Algorithms Resource Allocation Computer Simulation Crowding Italy Humans Patient Acuity Reproducibility of Results Emergency Service, Hospital
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A study on deep learning model based on global-local structure for crowd flow prediction.

Scientific reports Jun 1, 2024
Crowd flow prediction has been studied for a variety of purposes, ranging from the private sector such as location selection of stores according to the characteristics of commercial districts and customer-tailored marketing to the public sector for s...
Humans Deep Learning COVID-19 Crowding SARS-CoV-2
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Modular hierarchical reinforcement learning for multi-destination navigation in hybrid crowds.

Neural networks : the official journal of the International Neural Network Society Dec 20, 2023
Real-world robot applications usually require navigating agents to face multiple destinations. Besides, the real-world crowded environments usually contain dynamic and static crowds that implicitly interact with each other during navigation. To addre...
Crowding Computer Simulation Motion Deep Learning Reinforcement, Psychology
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Data-based tools can prevent crowd crushes.

Science (New York, N.Y.) Dec 8, 2022
Humans Crowding Databases, Factual Artificial Intelligence Mass Casualty Incidents
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Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting.

Sensors (Basel, Switzerland) Apr 22, 2022
In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context...
Awareness Neural Networks, Computer Crowding Image Processing, Computer-Assisted
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