ArticleThe Journal of clinical investigation2021
Machine learning-driven identification of early-life air toxic combinations associated with childhood asthma outcomes.
Article in The Journal of clinical investigation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed, 18 citations in OpenAlex.
- Early Life Exposure to Air Pollutants and Childhood Asthma.Current allergy and asthma reports · 2026Review
- Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.Allergy · 2026Review
- Leveraging artificial intelligence for the management of preschool wheeze: A narrative review.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Review
- The Effects of Environmental Factors on General Human Health: A Scoping Review.Healthcare (Basel, Switzerland) · 2024Article
- Machine learning-driven identification of air toxic combinations associated with asthma symptoms among elementary school children in Spokane, Washington, USA.The Science of the total environment · 2024Article
- Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop.The Journal of allergy and clinical immunology · 2024Article
- An exposome atlas of serum reveals the risk of chronic diseases in the Chinese population.Nature communications · 2024Article
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 2 institutions in 2 countries.
Funding
Abstract
Air pollution is a well-known contributor to asthma. Air toxics are hazardous air pollutants that cause or may cause serious health effects. Although individual air toxics have been associated with asthma, only a limited number of studies have specifically examined combinations of air toxics associated with the disease. We geocoded air toxic levels from the US National Air Toxics Assessment (NATA) to residential locations for participants of our AiRway in Asthma (ARIA) study. We then applied Data-driven ExposurE Profile extraction (DEEP), a machine learning-based method, to discover combinations of early-life air toxics associated with current use of daily asthma controller medication, lifetime emergency department visit for asthma, and lifetime overnight hospitalization for asthma. We discovered 20 multi-air toxic combinations and 18 single air toxics associated with at least 1 outcome. The multi-air toxic combinations included those containing acrylic acid, ethylidene dichloride, and hydroquinone, and they were significantly associated with asthma outcomes. Several air toxic members of the combinations would not have been identified by single air toxic analyses, supporting the use of machine learning-based methods designed to detect combinatorial effects. Our findings provide knowledge about air toxic combinations associated with childhood 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.