COPD

Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review.

There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however, currently limited evidence of the effectiveness of these systems. The aim of this study was to investigate the effectiveness of artificial intelligence-based computerised decision support systems in health and social care settings. We conducted a s...

An effective approach for CT lung segmentation using mask region-based convolutional neural networks.

Computer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal imaging method used to assist in the diagnosis of diseases such as bone fractures, lung cancer, heart disease, and emphysema, among others. Lung cancer is one of the four main causes of death in the world. The lung regions in the CT images are marke...

Semi-Supervised Learning for Semantic Segmentation of Emphysema With Partial Annotations.

Segmentation and quantification of each subtype of emphysema is helpful to monitor chronic obstructive pulmonary disease. Due to the nature of emphyse...

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...

Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.

Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice...

Green synthesis and characterization of gold nanoparticles using endophytic fungi and its in-vitro anticancer and biomedical applications.

The present study aimed to explore the anticancer potentials of the gold nanoparticles (NPs) obtained by green synthesis method using an endophytic st...

Identification of Catecholamine Neurotransmitters Using a Fluorescent Electronic Tongue.

Catecholamine neurotransmitters, specifically, dopamine (DA), epinephrine (EP), and norepinephrine (NE), are known as substantial indicators of variou...

A temporal visualization of chronic obstructive pulmonary disease progression using deep learning and unstructured clinical notes.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that is classified into stages based on disease severity. We ai...

Investigating the effect of gold nanoparticles on hydatid cyst protoscolices under low-power green laser irradiation.

OBJECTIVES: Various scolicidal agents are applied for the destruction of protoscolices in cysts media. Undesirable complications of the scolicidal age...

Using machine learning approaches to predict high-cost chronic obstructive pulmonary disease patients in China.

The accurate identification and prediction of high-cost Chronic obstructive pulmonary disease (COPD) patients is important for addressing the economic...

Machine Learning-Based Prediction Models for 30-Day Readmission after Hospitalization for Chronic Obstructive Pulmonary Disease.

While machine learning approaches can enhance prediction ability, little is known about their ability to predict 30-day readmission after hospitalizat...

Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.

The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e...

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...

Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation.

Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast...

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...

Ovarian Toxicity Assessment in Histopathological Images Using Deep Learning.

As ovarian toxicity is often a safety concern for cancer therapeutics, identification of ovarian pathology is important in early stages of preclinical...

An Individualized Prediction Model for Long-term Lung Function Trajectory and Risk of COPD in the General Population.

BACKGROUND: Prediction of future lung function will enable the identification of individuals at high risk of developing COPD, but the trajectory of lu...

EUSKOR: End-to-end coreference resolution system for Basque.

This paper describes the process of adapting the Stanford Coreference resolution module to the Basque language, taking into account the characteristic...

Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem.

Volumetric imaging is an essential diagnostic tool for medical practitioners. The use of popular techniques such as convolutional neural networks (CNN...

Learning to Quantify Emphysema Extent: What Labels Do We Need?

Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression, and to predict lung canc...