ArticleScientific reports2021
Asthma-prone areas modeling using a machine learning model.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.Allergy · 2026Review
- Mapping respiratory health digital interventions in South and Southeast Asia: a scoping review.Journal of global health · 2025Article
- The Effects of Environmental Factors on General Human Health: A Scoping Review.Healthcare (Basel, Switzerland) · 2024Article
- Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data.The journal of allergy and clinical immunology. In practice · 2024Article
- Spatial distribution of hospital admissions for asthma in the central area of Asturias, Northern Spain.BMC public health · 2023Article
- Geographic Medical Overview of Noncommunicable Diseases (Cardiovascular Diseases and Diabetes) in the Territory of the AP Vojvodina (Northern Serbia).Healthcare (Basel, Switzerland) · 2022Article
- Novelty in Public Health and Epidemiology Informatics.Yearbook of medical informatics · 2022Article
- Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020.Physics and chemistry of the earth (2002) · 2022Article
- COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms.International journal of environmental research and public health · 2021Article
- Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers.F1000Research · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran considering environmental, spatial factors. Initially, we built a spatial database using 872 locations of children with asthma and 13 environmental factors affecting the disease-distance to parks and streets, rainfall, temperature, humidity, pressure, wind speed, particulate matter (PM 10 and PM 2.5), ozone (O
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Registered trials
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