ReviewCurrent allergy and asthma reports2015
Distinguishing Asthma Phenotypes Using Machine Learning Approaches.
Review in Current allergy and asthma reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- A meta-analysis of genome-wide association studies of childhood wheezing phenotypes identifieseLife · 2023Pooled it
- Differential associations of allergic disease genetic variants with developmental profiles of eczema, wheeze and rhinitis.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2019Trial
- Features of asthma which provide meaningful insights for understanding the disease heterogeneity.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2018Trial
- Good things come in threes: evaluating clinical utility of machine learning-derived clusters.JAMIA open · 2026Review
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Clustering lung function and symptom profiles for asthma risk stratification.Scientific reports · 2025Article
- Patient-Reported Psychosocial Burdens and Quality of Life and Work Productivity Impacts Among Patients with Clinically Distinct Alopecia Areata Severity Profiles.Advances in therapy · 2025Article
- Patterns of Respiratory Symptoms and Asthma Diagnosis in School-Age Children: Three Birth Cohorts.Allergy · 2025Article
- Demographics and Clinical Characteristics among Patients with Distinct Psychosocial Burden Profiles Related to Vitiligo: Results of a Latent Class Analysis.Dermatology and therapy · 2025Article
- The role of wheezing subtypes in the development of early childhood asthma.Respiratory research · 2025Article
- Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review.Journal of asthma and allergy · 2025Review
- Unspecified asthma, childhood-onset, and adult-onset asthma have different causal genes: a Mendelian randomization analysis.Frontiers in immunology · 2024Article
- Novel Artificial Intelligence-Based Technology to Diagnose Asthma Using Methacholine Challenge Tests.Allergy, asthma & immunology research · 2024Article
- Early childhood wheezing phenotypes and determinants in a South African birth cohort: longitudinal analysis of the Drakenstein Child Health Study.The Lancet. Child & adolescent health · 2023Article
- Characteristics of Allergic, Eosinophilic, and Overlapping Asthma Phenotypes Among Pediatric Patients with Current Asthma: A Cross-Sectional Study from Saudi Arabia.Journal of asthma and allergy · 2023Article
- Article
- Establishing subclasses of childhood eczema, their risk factors and prognosis.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2022Review
- Subtypes of Asthma and Cold Weather-Related Respiratory Symptoms.International journal of environmental research and public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as 'asthma endotypes'. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies.
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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.