ReviewPediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology2022
Machine learning: A modern approach to pediatric asthma.
Review in Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2024Pooled it
- Development of a peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia.American journal of physiology. Lung cellular and molecular physiology · 2023Trial
- Innovative diagnostic techniques and their clinical implications in food allergy: current clinical practice and future perspectives.Frontiers in allergy · 2026Review
- Artificial intelligence and wheezing in children: where are we now?Frontiers in medicine · 2024Review
- Machine learning: A modern approach to pediatric asthma.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Among modern methods of statistical and computational analysis, the application of machine learning (ML) to healthcare data has been gaining recognition in helping us understand the heterogeneity of asthma and predicting its progression. In pediatric research, ML approaches may provide rapid advances in uncovering asthma phenotypes with potential translational impact in clinical practice. Also, several accurate models to predict asthma and its progression have been developed using ML. Here, we provide a brief overview of ML approaches recently proposed to characterize pediatric asthma.
Indexed as
Identifiers
What OpenQuestion holds
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.