Asthma

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 main objectives of this study were to use a feature engineering approach to incorporate clinical expert knowledge prior to applying machine learning techniques, and to assess the impact of the approach on model complexity and performance. Four machine ...

Deep CNN Sparse Coding for Real Time Inhaler Sounds Classification.

Effective management of chronic constrictive pulmonary conditions lies in proper and timely administration of medication. As a series of studies indicates, medication adherence can effectively be monitored by successfully identifying actions performed by patients during inhaler usage. This study focuses on the recognition of inhaler audio events during usage of pressurized metered dose inhalers (p...

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...

Evaluation of a neural network-based photon beam profile deconvolution method.

PURPOSE: The authors have previously shown the feasibility of using an artificial neural network (ANN) to eliminate the volume average effect (VAE) of...

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...

Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning.

The primary objective of this paper is to build classification models and strategies to identify breathing sound anomalies (wheeze, crackle) for autom...

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 Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators.

Patients with chronic obstructive pulmonary disease (COPD) repeat acute exacerbations (AE). Global Initiative for Chronic Obstructive Lung Disease (GO...

Rapid Detection From Clinical Mastitis Milk by Colloidal Gold Nanoparticle-Based Immunochromatographic Strips.

Rapid diagnostic technologies for bovine mastitis caused by () are urgently needed. In the current study, we generated an anti-ribosomal protein-L7/L...

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...

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.

Junctionless Poly-GeSn Ferroelectric Thin-Film Transistors with Improved Reliability by Interface Engineering for Neuromorphic Computing.

Ferroelectric HfZrO (Fe-HZO) with a larger remnant polarization () is achieved by using a poly-GeSn film as a channel material as compared with a poly...

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...

Inverse association between infection and childhood asthma in Greece: a case-control study.

INTRODUCTION: infection is a well-established etiological factor for a variety of diseases such as peptic ulcer and gastric cancer. On the other hand...

Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma.

BACKGROUND: Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and ...

Excitation Emission Matrix Fluorescence Spectroscopy for Combustion Generated Particulate Matter Source Identification.

The inhalation of particulate matter (PM) is a significant health risk associated with reduced life expectancy due to increased cardio-pulmonary disea...

Dehydroepiandrosterone Supplementation May Benefit Women with Asthma Who Have Low Androgen Levels: A Pilot Study.

INTRODUCTION: Among individuals with severe asthma, FEV is low in individuals with low dehydroepiandrosterone (DHEA) sulfate (DHEAS) levels. In the Se...

A comparative study of machine learning classifiers for risk prediction of asthma disease.

Asthma is a chronic disease characterized by wheezing, chest tightening and difficulty in breathing due to inflammation of lung airways. Early risk pr...

All-Assay-Max2 pQSAR: Activity Predictions as Accurate as Four-Concentration ICs for 8558 Novartis Assays.

Profile-quantitative structure-activity relationship (pQSAR) is a massively multitask, two-step machine learning method with unprecedented scope, accu...