Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.

Introduction: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the methodology.

Methods: 10 databases were searched.

Artificial intelligence in pediatric allergy research.

Unlabelled: Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They are heterogeneous diseases, can co-exist in their development, and manifest complex associations with other disorders and environmental and hereditary factors. Elucidating these intricacies by identifying clinically distinguishable groups and actionable risk factors will allow for better understanding of the diseases, which will enhance clinical management and benefit society and affected individuals and families.

Asthma and allergy trajectories in children based on combined parental report and register data.

Background: Trajectories of asthma and allergy in children are heterogeneous and commonly derived from parental report of disease or clinical records. This study combined parental-reported and register-based dispensed medication data to characterize childhood trajectories of co-existing asthma, allergic rhinitis, and eczema.

Methods: From a Swedish population-based birth cohort (N = 5654), survey responses collected at the age of 1, 4.

Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review.

Introduction: Development of asthma and allergies in childhood/adolescence commonly follows a sequential progression termed the 'atopic march'. Recent reports indicate, however, that these diseases are composed of multiple distinct phenotypes, with possibly differential trajectories. We aim to synthesise the current literature in the field of machine learning-based trajectory studies of asthma/allergies in children and adolescents, summarising the frequency, characteristics and associated risk factors and outcomes of identified trajectories and indicating potential directions for subsequent research in replicability, pathophysiology, risk stratification and personalised management.

Level of education, but not occupation, is differentially associated with asthma phenotypes in adults.

Education, but not occupation, was differentially associated with adult asthma phenotypes in the general population. Further research into socioeconomic status variation in various asthma phenotypes is warranted.