Challenges in interpreting allergen microarrays in relation to clinical symptoms: A machine learning approach

Pediatric Allergy and Immunology

Volumn 25, Issue 1, 2014, Pages 71-79

  Prosperi, Mattia C F ^a,b   Belgrave, Danielle ^a,b   Buchan, Iain ^a   Simpson, Angela ^b   Custovic, Adnan ^b  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Airway hyper reactivity; Asthma; Bayesian networks; Children; Component resolved diagnostics; Feature selection; IgE; Logistic regression; Machine learning; Methacholine; Random forests; Rhinitis; Wheeze

Indexed keywords

AIRWAY HYPER-REACTIVITY; ASTHMA; BAYESIAN NETWORKS; CHILDREN; COMPONENT-RESOLVED DIAGNOSTICS; FEATURE SELECTION; IGE; LOGISTIC REGRESSION; MACHINE LEARNING; METHACHOLINE; RANDOM FORESTS; RHINITIS; WHEEZE;

ALLERGENS; ANIMALS; ARTIFICIAL INTELLIGENCE; ASTHMA; AUTOMATION, LABORATORY; BRONCHIAL HYPERREACTIVITY; BRONCHIAL PROVOCATION TESTS; CHILD; COHORT STUDIES; DIAGNOSTIC TESTS, ROUTINE; FEMALE; HUMANS; HYPERSENSITIVITY; IMMUNOGLOBULIN E; INDIVIDUALIZED MEDICINE; MALE; MICROARRAY ANALYSIS; POPULATION GROUPS; PREDICTIVE VALUE OF TESTS; REPRODUCIBILITY OF RESULTS;

EID: 84894241264     PISSN: 09056157     EISSN: 13993038     Source Type: Journal    
DOI: 10.1111/pai.12139     Document Type: Article

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