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Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Early emergency department treatment of acute asthma with systemic corticosteroids
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Applied Predictive Modeling
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How artificial intelligence could transform emergency department operations
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Early Administration of Systemic Corticosteroids Reduces Hospital Admission Rates for Children With Moderate and Severe Asthma Exacerbation
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Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index
inferredcites →https://doi.org/10.1016/j.hrtlng.2006.02.002openalex-snapshot
Predictors of emergency department visit disposition for patients with chronic obstructive pulmonary disease
inferredcites →https://doi.org/10.1016/j.jaci.2014.08.028openalex-snapshot
Comparison of US emergency department acute asthma care quality: 1997-2001 and 2011-2012
inferredcites →https://doi.org/10.1016/j.jaip.2014.09.017openalex-snapshot
Development and Internal Validation of a Pediatric Acute Asthma Prediction Rule for Hospitalization
inferredcites →https://doi.org/10.1016/j.jemermed.2016.02.026openalex-snapshot
An Electronic Emergency Triage System to Improve Patient Distribution by Critical Outcomes
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Early use of non-invasive ventilation for acute exacerbations of chronic obstructive pulmonary disease on general respiratory wards: a multicentre randomised controlled trial
inferredcites →https://doi.org/10.1097/01.mlr.0000182534.19832.83openalex-snapshot
Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data
inferredcites →https://doi.org/10.1109/jbhi.2014.2303481openalex-snapshot
Risk Scoring for Prediction of Acute Cardiac Complications from Imbalanced Clinical Data
inferredcites →https://doi.org/10.1111/acem.12249openalex-snapshot
Decreasing Length of Stay in the Emergency Department With a Split Emergency Severity Index 3 Patient Flow Model
inferredcites →https://doi.org/10.1111/acem.12876openalex-snapshot
Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach
inferredcites →https://doi.org/10.1111/acem.12920openalex-snapshot
Association of Guideline‐concordant Acute Asthma Care in the Emergency Department With Shorter Hospital Length of Stay: A Multicenter Observational Study
inferredcites →https://doi.org/10.1111/j.1553-2712.2008.00284.xopenalex-snapshot
National Study of Emergency Department Visits for Acute Exacerbation of Chronic Obstructive Pulmonary Disease, 1993–2005
inferredcites →https://doi.org/10.1111/jan.12304openalex-snapshot
Triage: an investigation of the process and potential vulnerabilities
inferredcites →https://doi.org/10.1161/circoutcomes.116.003039openalex-snapshot
Analysis of Machine Learning Techniques for Heart Failure Readmissions
inferredcites →https://doi.org/10.1513/annalsats.201306-166ocopenalex-snapshot
A Population-based Study of Adults Who Frequently Visit the Emergency Department for Acute Asthma. California and Florida, 2009–2010
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Comparing predictions made by a prediction model, clinical score, and physicians
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Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review
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Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study
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Applications of Machine Learning Approaches in Emergency Medicine; a Review Article.
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A machine-learning-based prediction method for easy COPD classification based on pulse oximetry clinical use
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Quantitative Computed Tomography Imaging and Machine Learning for Evaluating Chronic Obstructive Pulmonary Disease
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Quantitative Computed Tomography Imaging and Machine Learning for Evaluating Chronic Obstructive Pulmonary Disease
inferred← citeshttps://doi.org/10.3389/fdata.2021.643558openalex-snapshot
Predicting Admissions From a Paediatric Emergency Department – Protocol for Developing and Validating a Low-Dimensional Machine Learning Prediction Model
inferred← citeshttps://doi.org/10.3389/fmed.2023.1105854openalex-snapshot
Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study
inferred← citeshttps://doi.org/10.3389/fneur.2023.1247492openalex-snapshot
Machine learning-based prediction of symptomatic intracerebral hemorrhage after intravenous thrombolysis for stroke: a large multicenter study
inferred← citeshttps://doi.org/10.3389/fneur.2024.1408457openalex-snapshot
Machine learning-based prediction of early neurological deterioration after intravenous thrombolysis for stroke: insights from a large multicenter study
inferred← citeshttps://doi.org/10.3389/fpubh.2022.927874openalex-snapshot
Predicting the behavioral intentions of hospice and palliative care providers from real-world data using supervised learning: A cross-sectional survey study
inferred← citeshttps://doi.org/10.3390/app15137040openalex-snapshot
Enhancing COPD Diagnosis Accuracy: A Study on Regression Analysis and the Generalized Linear Model Technique
inferred← citeshttps://doi.org/10.3390/children8050376openalex-snapshot
Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma
inferred← citeshttps://doi.org/10.3390/diagnostics11122396openalex-snapshot
Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease
inferred← citeshttps://doi.org/10.3390/diagnostics12010082openalex-snapshot
A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain
inferred← citeshttps://doi.org/10.3390/informatics7040056openalex-snapshot
Using Mobiles to Monitor Respiratory Diseases
inferred← citeshttps://doi.org/10.3390/jcm13226913openalex-snapshot
Assessing the Impact of New Technologies on Managing Chronic Respiratory Diseases
inferred← citeshttps://doi.org/10.3390/medicina57040351openalex-snapshot
Machine Learning and Syncope Management in the ED: The Future Is Coming
inferred← citeshttps://doi.org/10.3390/su14169951openalex-snapshot
Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review
inferred← citeshttps://doi.org/10.4018/979-8-3373-7759-9.ch010openalex-snapshot
Machine Learning Applications in Asthma and COPD Management
inferred← citeshttps://doi.org/10.4081/monaldi.2022.2339openalex-snapshot
Hidden biases in clinical decision-making: potential solutions, challenges, and perspectives
inferred← citeshttps://doi.org/10.47750/pnr.2022.13.04.001openalex-snapshot
A predictive model for the early prognosis and characterization of asthma
inferred← citeshttps://doi.org/10.52827/hititmedj.1467697openalex-snapshot
Acil Tıp Uygulamalarında Makine Öğrenimleri Başrolü Alır mı?
inferred← citeshttps://doi.org/10.6084/m9.figshare.12257924openalex-snapshot
Additional file 1 of Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies
inferred← citeshttps://doi.org/10.6084/m9.figshare.13296966openalex-snapshot
Additional file 1 of Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis
inferred← citeshttps://doi.org/10.6084/m9.figshare.21622620openalex-snapshot
Additional file 1 of MRI-assessed diaphragmatic function can predict frequent acute exacerbation of COPD: a prospective observational study based on telehealth-based monitoring system
inferred← citeshttps://doi.org/10.6084/m9.figshare.21622623openalex-snapshot
Additional file 2 of MRI-assessed diaphragmatic function can predict frequent acute exacerbation of COPD: a prospective observational study based on telehealth-based monitoring system
inferred← citeshttps://doi.org/10.6084/m9.figshare.21622626openalex-snapshot
Additional file 3 of MRI-assessed diaphragmatic function can predict frequent acute exacerbation of COPD: a prospective observational study based on telehealth-based monitoring system
inferred← citeshttps://doi.org/10.6084/m9.figshare.23488870openalex-snapshot
Additional file 1 of Two machine learning-based nomogram to predict risk and prognostic factors for liver metastasis from pancreatic neuroendocrine tumors: a multicenter study
inferred← citeshttps://doi.org/10.6084/m9.figshare.23488876openalex-snapshot
Additional file 2 of Two machine learning-based nomogram to predict risk and prognostic factors for liver metastasis from pancreatic neuroendocrine tumors: a multicenter study
inferred← citeshttps://doi.org/10.6084/m9.figshare.24515962openalex-snapshot
Additional file 1 of An explainable artificial intelligence framework for risk prediction of COPD in smokers
inferred← citeshttps://doi.org/10.6084/m9.figshare.26566259openalex-snapshot
Additional file 1 of Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets
inferred← citeshttps://doi.org/10.62110/sciencein.jist.2024.v12.780openalex-snapshot
Multi Feature fusion for COPD Classification using Deep learning algorithms
inferred← citeshttps://doi.org/10.68159/h706285658openalex-snapshot
Transparent Machine Learning Model for Predicting Delayed Follow-Up After Emergency Department Visits Using Discharge Instructions, Appointment Availability, Portal Engagement, Social Risk Data, and Visit Severity
inferred← citeshttps://doi.org/10.68159/m177384136openalex-snapshot
Artificial Intelligence for Hospital Workflow Analytics: A Systematic Review of Machine Learning Models for Patient Flow, Staff Scheduling, Resource Utilization, and Operational Delay Prediction
inferred← citeshttps://doi.org/10.70675/95b9881fz3c35z4a2aza74dz076482ae1892openalex-snapshot
Intelligence des données au service de la gestion optimisée des urgences hospitalières
inferred← citeshttps://doi.org/10.7759/cureus.59906openalex-snapshot
Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review
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