56 research outputs found
Asthma Phenotypes in Childhood
INTRODUCTION: Asthma is no longer thought of as a single disease, but rather a collection of varying symptoms expressing different disease patterns. One of the ongoing challenges is understanding the underlying pathophysiological mechanisms that may be responsible for the varying responses to treatment. Areas Covered: This review provides an overview of our current understanding of the asthma phenotype concept in childhood and describes key findings from both conventional and data-driven methods. Expert Commentary: With the vast amounts of data generated from cohorts, there is hope that we can elucidate distinct pathophysiological mechanisms, or endotypes. In return, this would lead to better patient stratification and disease management, thereby providing true personalised medicine
Generative models improve fairness of medical classifiers under distribution shifts
A ubiquitous challenge in machine learning is the problem of domain
generalisation. This can exacerbate bias against groups or labels that are
underrepresented in the datasets used for model development. Model bias can
lead to unintended harms, especially in safety-critical applications like
healthcare. Furthermore, the challenge is compounded by the difficulty of
obtaining labelled data due to high cost or lack of readily available domain
expertise. In our work, we show that learning realistic augmentations
automatically from data is possible in a label-efficient manner using
generative models. In particular, we leverage the higher abundance of
unlabelled data to capture the underlying data distribution of different
conditions and subgroups for an imaging modality. By conditioning generative
models on appropriate labels, we can steer the distribution of synthetic
examples according to specific requirements. We demonstrate that these learned
augmentations can surpass heuristic ones by making models more robust and
statistically fair in- and out-of-distribution. To evaluate the generality of
our approach, we study 3 distinct medical imaging contexts of varying
difficulty: (i) histopathology images from a publicly available generalisation
benchmark, (ii) chest X-rays from publicly available clinical datasets, and
(iii) dermatology images characterised by complex shifts and imaging
conditions. Complementing real training samples with synthetic ones improves
the robustness of models in all three medical tasks and increases fairness by
improving the accuracy of diagnosis within underrepresented groups. This
approach leads to stark improvements OOD across modalities: 7.7% prediction
accuracy improvement in histopathology, 5.2% in chest radiology with 44.6%
lower fairness gap and a striking 63.5% improvement in high-risk sensitivity
for dermatology with a 7.5x reduction in fairness gap
Patterns of IgE responses to multiple allergen components and clinical symptoms at age 11 years
BackgroundThe relationship between sensitization to allergens and disease is complex.ObjectiveWe sought to identify patterns of response to a broad range of allergen components and investigate associations with asthma, eczema, and hay fever.MethodsSerum specific IgE levels to 112 allergen components were measured by using a multiplex array (Immuno Solid-phase Allergen Chip) in a population-based birth cohort. Latent variable modeling was used to identify underlying patterns of component-specific IgE responses; these patterns were then related to asthma, eczema, and hay fever.ResultsTwo hundred twenty-one of 461 children had IgE to 1 or more components. Seventy-one of the 112 components were recognized by 3 or more children. By using latent variable modeling, 61 allergen components clustered into 3 component groups (CG1, CG2, and CG3); protein families within each CG were exclusive to that group. CG1 comprised 27 components from 8 plant protein families. CG2 comprised 7 components of mite allergens from 3 protein families. CG3 included 27 components of plant, animal, and fungal origin from 12 protein families. Each CG included components from different biological sources with structural homology and also nonhomologous proteins arising from the same biological source. Sensitization to CG3 was most strongly associated with asthma (odds ratio [OR], 8.20; 95% CI, 3.49-19.24; PÂ <Â .001) and lower FEV1 (PÂ <Â .001). Sensitization to CG1 was associated with hay fever (OR, 12.79; 95% CI, 6.84-23.90; PÂ <Â .001). Sensitization to CG2 was associated with both asthma (OR, 3.60; 95% CI, 2.05-6.29) and hay fever (OR, 2.52; 95% CI, 1.38-4.61).ConclusionsLatent variable modeling with a large number of allergen components identified 3 patterns of IgE responses, each including different protein families. In 11-year-old children the pattern of response to components of multiple allergens appeared to be associated with current asthma and hay fever but not eczema
Trajectories of childhood immune development and respiratory health relevant to asthma and allergy.
Events in early life contribute to subsequent risk of asthma; however, the causes and trajectories of childhood wheeze are heterogeneous and do not always result in asthma. Similarly, not all atopic individuals develop wheeze, and vice versa. The reasons for these differences are unclear. Using unsupervised model-based cluster analysis, we identified latent clusters within a prospective birth cohort with deep immunological and respiratory phenotyping. We characterised each cluster in terms of immunological profile and disease risk, and replicated our results in external cohorts from the UK and USA. We discovered three distinct trajectories, one of which is a high-risk 'atopic' cluster with increased propensity for allergic diseases throughout childhood. Atopy contributes varyingly to later wheeze depending on cluster membership. Our findings demonstrate the utility of unsupervised analysis in elucidating heterogeneity in asthma pathogenesis and provide a foundation for improving management and prevention of childhood asthma
Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies
The term "atopic march" has been used to imply a natural progression of a cascade of symptoms from eczema to asthma and rhinitis through childhood. We hypothesize that this expression does not adequately describe the natural history of eczema, wheeze, and rhinitis during childhood. We propose that this paradigm arose from cross-sectional analyses of longitudinal studies, and may reflect a population pattern that may not predominate at the individual level.Data from 9,801 children in two population-based birth cohorts were used to determine individual profiles of eczema, wheeze, and rhinitis and whether the manifestations of these symptoms followed an atopic march pattern. Children were assessed at ages 1, 3, 5, 8, and 11 y. We used Bayesian machine learning methods to identify distinct latent classes based on individual profiles of eczema, wheeze, and rhinitis. This approach allowed us to identify groups of children with similar patterns of eczema, wheeze, and rhinitis over time. Using a latent disease profile model, the data were best described by eight latent classes: no disease (51.3%), atopic march (3.1%), persistent eczema and wheeze (2.7%), persistent eczema with later-onset rhinitis (4.7%), persistent wheeze with later-onset rhinitis (5.7%), transient wheeze (7.7%), eczema only (15.3%), and rhinitis only (9.6%). When latent variable modelling was carried out separately for the two cohorts, similar results were obtained. Highly concordant patterns of sensitisation were associated with different profiles of eczema, rhinitis, and wheeze. The main limitation of this study was the difference in wording of the questions used to ascertain the presence of eczema, wheeze, and rhinitis in the two cohorts.The developmental profiles of eczema, wheeze, and rhinitis are heterogeneous; only a small proportion of children (∼ 7% of those with symptoms) follow trajectory profiles resembling the atopic march. Please see later in the article for the Editors' Summary
Challenges in interpreting wheeze phenotypes: The clinical implications of statistical learning techniques
Characterizing wheeze phenotypes to identify endotypes of childhood asthma, and the implications for future management
Atopic Dermatitis and Respiratory Allergy: What is the Link.
Understanding the aetiology and progression of atopic dermatitis and respiratory allergy may elucidate early preventative and management strategies aimed towards reducing the global burden of asthma and allergic disease. In this article, we review the current opinion concerning the link between atopic dermatitis and the subsequent progression of respiratory allergies during childhood and into early adolescence. Advances in machine learning and statistical methodology have facilitated the discovery of more refined definitions of phenotypes for identifying biomarkers. Understanding the role of atopic dermatitis in the development of respiratory allergy may ultimately allow us to determine more effective treatment strategies, thus reducing the patient and economic burden associated with these conditions
Causal Discovery for Causal Bandits utilizing Separating Sets
The Causal Bandit is a variant of the classic Bandit problem where an agent
must identify the best action in a sequential decision-making process, where
the reward distribution of the actions displays a non-trivial dependence
structure that is governed by a causal model. All methods proposed thus far in
the literature rely on exact prior knowledge of the causal model to obtain
improved estimators for the reward. We formulate a new causal bandit algorithm
that is the first to no longer rely on explicit prior causal knowledge and
instead uses the output of causal discovery algorithms. This algorithm relies
on a new estimator based on separating sets, a causal structure already known
in causal discovery literature. We show that given a separating set, this
estimator is unbiased, and has lower variance compared to the sample mean. We
derive a concentration bound and construct a UCB-type algorithm based on this
bound, as well as a Thompson sampling variant. We compare our algorithms with
traditional bandit algorithms on simulation data. On these problems, our
algorithms show a significant boost in performance
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