Atopy

Detecting asthma exacerbations using daily home monitoring and machine learning.

OBJECTIVE: Acute exacerbations contribute significantly to the morbidity of asthma. Recent studies have shown that early detection and treatment of asthma exacerbations leads to improved outcomes. We aimed to develop a machine learning algorithm to detect severe asthma exacerbations using easily available daily monitoring data.

Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study.

BACKGROUND: Asthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients' quality of life. However, efficient methods that can help identify personalized risk factors and make early predictions are lacking.

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology.

BACKGROUND: There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing...

Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort.

INTRODUCTION: Most asthma attacks and subsequent deaths are potentially preventable. We aim to develop a prognostic tool for identifying patients at h...

Deep learning for genomics using Janggu.

In recent years, numerous applications have demonstrated the potential of deep learning for an improved understanding of biological processes. However...

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population.

Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other comm...

Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin-Stained Sections.

PURPOSE: Although high T-cell density is a well-established favorable prognostic factor in colorectal cancer, the prognostic significance of tumor-ass...

Pulmonary Embolism in Acute Asthma Exacerbation: Clinical Characteristics, Prediction Model and Hospital Outcomes.

PURPOSE: Little is known about the characteristics and impact of acute pulmonary embolism (PE) during episodes of asthma exacerbation. We aimed to cha...

Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning.

Intelligent medical diagnosis has become common in the era of big data, although this technique has been applied to asthma only in limited contexts. U...

Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance.

Incorporating expert knowledge at the time machine learning models are trained holds promise for producing models that are easier to interpret. The ma...

Deep Learning to Unveil Correlations between Urban Landscape and Population Health.

The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Severa...

Deep learning approaches for sleep disorder prediction in an asthma cohort.

OBJECTIVE: Sleep is a natural activity of humans that affects physical and mental health; therefore, sleep disturbance may lead to fatigue and lower p...

Artificial intelligence and machine learning in respiratory medicine.

: The application of artificial intelligence (AI) and machine learning (ML) in medicine and in particular in respiratory medicine is an increasingly r...

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.

High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resu...

A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data.

'Asthma' is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previou...

Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study.

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how th...

Vitamin D and Serum Immunoglobulin E Levels in Allergic Rhinitis: A Case-control Study from Pakistan.

Introduction Allergic rhinitis (AR) is the most common non-infectious rhinitis and is associated with sneezing, cough, and flu-like symptoms. The exac...

Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.

The wide adoption of electronic health record systems in health care generates big real-world data that open new venues to conduct clinical research. ...

The doctor will see you now: How machine learning and artificial intelligence can extend our understanding and treatment of asthma.

Machine learning approach to single nucleotide polymorphism-based asthma prediction.

Machine learning (ML) is poised as a transformational approach uniquely positioned to discover the hidden biological interactions for better predictio...