ArticleBMC medical genomics2014
Predicting phenotypes of asthma and eczema with machine learning.
Article in BMC medical genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Improving machine-learning development in allergology: bridging the gap between open-access and cohort-based databases.Current opinion in allergy and clinical immunology · 2026Review
- Machine learning for detection of diffusion abnormalities-related respiratory changes among normal, overweight, and obese individuals based on BMI and pulmonary ventilation parameters: an observational study.BMC medical informatics and decision making · 2025Observational
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study.BMC medical research methodology · 2024Article
- Novel Artificial Intelligence-Based Technology to Diagnose Asthma Using Methacholine Challenge Tests.Allergy, asthma & immunology research · 2024Article
- The combination of machine learning and untargeted metabolomics identifies the lipid metabolism -related gene CH25H as a potential biomarker in asthma.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2023Article
- Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA Study.Life (Basel, Switzerland) · 2022Article
- Development of childhood asthma prediction models using machine learning approaches.Clinical and translational allergy · 2021Article
- The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings.Journal of thoracic disease · 2021Article
- Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.International journal of medical sciences · 2021Review
- Prodromal clinical, demographic, and socio-ecological correlates of asthma in adults: a 10-year statewide big data multi-domain analysis.The Journal of asthma : official journal of the Association for the Care of Asthma · 2020Article
- The role of artificial intelligence in identifying asthma in pediatric inpatient setting.Annals of translational medicine · 2020Article
- Identification of Specific IgE Antibodies and Asthma Control Interaction and Association Using Cluster Analysis in a Bulgarian Asthmatic Children Cohort.Antibodies (Basel, Switzerland) · 2020Article
- Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach.BMC medical research methodology · 2019Article
- Blood Biomarkers of Sensitization and Asthma.Frontiers in pediatrics · 2019Review
- Article
- An Expert Diagnostic System to Automatically Identify Asthma and Chronic Obstructive Pulmonary Disease in Clinical Settings.Scientific reports · 2018Article
- Modeling asynchronous event sequences with RNNs.Journal of biomedical informatics · 2018Article
- Natural language processing of clinical notes for identification of critical limb ischemia.International journal of medical informatics · 2018Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThere is increasing recognition that asthma and eczema are heterogeneous diseases. We investigated the predictive ability of a spectrum of machine learning methods to disambiguate clinical sub-groups of asthma, wheeze and eczema, using a large heterogeneous set of attributes in an unselected population. The aim was to identify to what extent such heterogeneous information can be combined to reveal specific clinical manifestations.
methodsThe study population comprised a cross-sectional sample of adults, and included representatives of the general population enriched by subjects with asthma. Linear and non-linear machine learning methods, from logistic regression to random forests, were fit on a large attribute set including demographic, clinical and laboratory features, genetic profiles and environmental exposures. Outcome of interest were asthma, wheeze and eczema encoded by different operational definitions. Model validation was performed via bootstrapping.
resultsThe study population included 554 adults, 42% male, 38% previous or current smokers. Proportion of asthma, wheeze, and eczema diagnoses was 16.7%, 12.3%, and 21.7%, respectively. Models were fit on 223 non-genetic variables plus 215 single nucleotide polymorphisms. In general, non-linear models achieved higher sensitivity and specificity than other methods, especially for asthma and wheeze, less for eczema, with areas under receiver operating characteristic curve of 84%, 76% and 64%, respectively. Our findings confirm that allergen sensitisation and lung function characterise asthma better in combination than separately. The predictive ability of genetic markers alone is limited. For eczema, new predictors such as bio-impedance were discovered.
conclusionsMore usefully-complex modelling is the key to a better understanding of disease mechanisms and personalised healthcare: further advances are likely with the incorporation of more factors/attributes and longitudinal measures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.