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.

Deriving and validating a risk prediction model for long COVID: a population-based, retrospective cohort study in Scotland.

Objectives: Using electronic health records, we derived and internally validated a prediction model to estimate risk factors for long COVID and predict individual risk of developing long COVID.

Design: Population-based, retrospective cohort study.

Setting: Scotland.

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 and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.

Background: The diagnosis of Parkinson's disease is currently based on clinical evaluation. Despite clinical hallmarks, unfortunately, the error rate is still significant. Low in-vivo diagnostic accuracy of clinical evaluation mainly relies on the lack of quantitative biomarkers for an objective motor performance assessment.

Machine Learning-Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review.

Background: An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks.

DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol.

Introduction: Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study 'DIGIPREDICT' is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provide an early, personalised warning of asthma attacks.

Trends in inequalities in avoidable hospitalisations across the COVID-19 pandemic: a cohort study of 23.5 million people in England.

Objective: To determine whether periods of disruption were associated with increased 'avoidable' hospital admissions and wider social inequalities in England.

Design: Observational repeated cross-sectional study.

Setting: England (January 2019 to March 2022).

Handling Class Imbalance in Machine Learning-based Prediction Models: A Case Study in Asthma Management.

A data-driven prediction tool has the potential to provide early warning of an asthma attack and improve asthma management and outcomes. Most previous machine learning (ML)-based studies for asthma attack prediction have reported a severe class imbalance, with major implications for model performance. We aimed to undertake a systematic comparison of several class imbalance handling techniques in the context of risk prediction models for asthma prognosis.

Predictors of incomplete COVID-19 vaccine schedule among adults in Scotland: Two retrospective cohort analyses of the primary schedule and third dose.

Background: Vaccination continues to be the key public health measure for preventing severe COVID-19 outcomes. Certain groups may be at higher risk of incomplete vaccine schedule, which may leave them vulnerable to COVID-19 hospitalisation and death.

Aim: To identify the sociodemographic and clinical predictors for not receiving a scheduled COVID-19 vaccine after previously receiving one.

Home monitoring with connected mobile devices for asthma attack prediction with machine learning.

Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious. Passive monitoring with mobile-health devices, especially when combined with machine-learning, provides an avenue to reduce management burden.

Effectiveness of mRNA boosters after homologous primary series with BNT162b2 or ChAdOx1 against symptomatic infection and severe COVID-19 in Brazil and Scotland: A test-negative design case-control study.

Background: Brazil and Scotland have used mRNA boosters in their respective populations since September 2021, with Omicron's emergence accelerating their booster program. Despite this, both countries have reported substantial recent increases in Coronavirus Disease 2019 (COVID-19) cases. The duration of the protection conferred by the booster dose against symptomatic Omicron cases and severe outcomes is unclear.

Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol.

Introduction: Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback.We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices.

BNT162b2 COVID-19 vaccination uptake, safety, effectiveness and waning in children and young people aged 12-17 years in Scotland.

Background: The two-dose BNT162b2 (Pfizer-BioNTech) vaccine has demonstrated high efficacy against COVID-19 disease in clinical trials of children and young people (CYP). Consequently, we investigated the uptake, safety, effectiveness and waning of the protective effect of the BNT162b2 against symptomatic COVID-19 in CYP aged 12-17 years in Scotland.

Methods: The analysis of the vaccine uptake was based on information from the Turas Vaccination Management Tool, inclusive of Mar 1, 2022.

Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data.

Introduction: COVID-19 is commonly experienced as an acute illness, yet some people continue to have symptoms that persist for weeks, or months (commonly referred to as 'long-COVID'). It remains unclear which patients are at highest risk of developing long-COVID. In this protocol, we describe plans to develop a prediction model to identify individuals at risk of developing long-COVID.

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review.

Background: Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring.

Impact of COVID-19 pandemic on asthma exacerbations: Retrospective cohort study of over 500,000 patients in a national English primary care database.

Background: Several countries reported a substantial reduction in asthma exacerbations associated with COVID-19 pandemic-related restrictions. However, it is not known if these early reported declines were short-term and if these have rebounded to pre-pandemic levels following easing of lockdown restrictions.

Methods: We undertook a retrospective, cohort study of all asthma patients in a national primary care database of almost 10 million patients, Optimum Patient Care Database (OPCRD), identified from January 1, 2010, to December 31, 2015, using a previously validated algorithm.