Machine learning for prediction of asthma exacerbations among asthmatic patients: a systematic review and meta-analysis
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Machine learning for prediction of asthma exacerbations among asthmatic patients: a systematic review and meta-analysis
Authors
Keywords
-
Journal
BMC Pulmonary Medicine
Volume 23, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2023-07-29
DOI
10.1186/s12890-023-02570-w
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Exacerbation Profile and Risk Factors in a T2-Low Severe Asthma Population
- (2022) P Jane McDowell et al. AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
- Prevalence of Patients with Uncontrolled Asthma Despite NVL/GINA Step 4/5 Treatment in Germany
- (2022) Karl-Christian Bergmann et al. Journal of Asthma and Allergy
- Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review
- (2022) Kevin CH Tsang et al. Journal of Asthma and Allergy
- Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations
- (2022) Anne A. H. de Hond et al. Scientific Reports
- Novel machine learning can predict acute asthma exacerbation
- (2021) Joe G. Zein et al. CHEST
- Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study
- (2021) Yao Tong et al. JOURNAL OF MEDICAL INTERNET RESEARCH
- Predicting asthma-related crisis events using routine electronic healthcare data
- (2021) Michael Noble et al. BRITISH JOURNAL OF GENERAL PRACTICE
- Short-term exposure to nitrogen dioxide and mortality: A systematic review and meta-analysis
- (2021) Mingrui Wang et al. ENVIRONMENTAL RESEARCH
- A systematic review of methodology used in the development of prediction models for future asthma exacerbation
- (2020) Joshua Bridge et al. BMC Medical Research Methodology
- Asthma epidemiology and risk factors
- (2020) Jessica Stern et al. Seminars in Immunopathology
- PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration
- (2019) Karel G.M. Moons et al. ANNALS OF INTERNAL MEDICINE
- Prevalence, disease burden, and treatment reality of patients with severe, uncontrolled asthma in Japan
- (2019) Hiroyuki Nagase et al. ALLERGOLOGY INTERNATIONAL
- Big Data and Machine Learning in Health Care
- (2018) Andrew L. Beam et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Utilization and Costs of Severe Uncontrolled Asthma in a Managed-Care Setting
- (2016) Robert S. Zeiger et al. Journal of Allergy and Clinical Immunology-In Practice
- Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints
- (2015) Tjeerd van der Ploeg et al. BMC Medical Research Methodology
- Impact of Asthma Exacerbations and Asthma Triggers on Asthma-related Quality of Life in Patients with Severe or Difficult-to-Treat Asthma
- (2014) Allan T. Luskin et al. Journal of Allergy and Clinical Immunology-In Practice
- Genome Wide Association Study to predict severe asthma exacerbations in children using random forests classifiers
- (2011) Mousheng Xu et al. BMC Medical Genetics
- Definitions of asthma exacerbations
- (2011) Rik JB Loymans et al. Current Opinion in Allergy and Clinical Immunology
- An Official American Thoracic Society/European Respiratory Society Statement: Asthma Control and Exacerbations
- (2009) Helen K. Reddel et al. AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
- Severe Exacerbations and Decline in Lung Function in Asthma
- (2008) Paul M. O'Byrne et al. AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started