ArticleThe World Allergy Organization journal2019
Investigation of house dust mite induced allergy using logistic regression in West Bengal, India.
Article in The World Allergy Organization journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- Artificial intelligence applications in allergic rhinitis diagnosis: Focus on ensemble learning.Asia Pacific allergy · 2024Article
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7 authors.
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Abstract
backgroundThe diagnosis of house dust mite (HDM) allergy based on Skin prick test (SPT) is not accurate, especially in lower risk cases. Our aim is to develop and validate a predictive model to diagnose the HDM allergic symptoms (urticaria, allergic rhinitis, asthma).
methodsA forward-step logistic regression model was developed using a data set of 537 patients of West Bengal, India consisting of clinical variables (SPT based on 6 allergens of house dust and house dust mites, total IgE) and demographic characteristics (age, sex, house conditions). The output probability was estimated from the allergic symptoms shown by the patients. We finally prospectively validated a data set of 600 patients.
resultsThe gradual inclusion of the variables increased the correlation between observed and predicted probabilities (correlation coefficient (r
conclusionThis is perhaps the first attempt to model the outcome of HDM allergy in terms of symptoms, which could open up an alternative but highly efficient way for accurate diagnosis of HDM allergy enhancing the efficiency of immunotherapy.
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