AIMC Topic: Pulmonary Disease, Chronic Obstructive

Clear Filters Showing 141 to 150 of 242 articles

Novel biomarker genes which distinguish between smokers and chronic obstructive pulmonary disease patients with machine learning approach.

BMC pulmonary medicine
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is combination of progressive lung diseases. The diagnosis of COPD is generally based on the pulmonary function testing, however, difficulties underlie in prognosis of smokers or early stage of...

The Emerging Role of Radiomics in COPD and Lung Cancer.

Respiration; international review of thoracic diseases
Medical imaging plays a key role in evaluating and monitoring lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. The application of artificial intelligence in medical imaging has transformed medical images into mineab...

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

Chest
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 they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year lo...

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

BMC medical informatics and decision making
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that is classified into stages based on disease severity. We aimed to characterize the time to progression prior to death in patients with COPD and to generate a t...

Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease.

Computer methods and programs in biomedicine
OBJECTIVES: Identifying acute exacerbations in chronic obstructive pulmonary disease (AECOPDs) is of utmost importance for reducing the associated mortality and financial burden. In this research, the authors aimed to develop identification models fo...

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

Health informatics journal
The accurate identification and prediction of high-cost Chronic obstructive pulmonary disease (COPD) patients is important for addressing the economic burden of COPD. The objectives of this study were to use machine learning approaches to identify an...

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

COPD
While machine learning approaches can enhance prediction ability, little is known about their ability to predict 30-day readmission after hospitalization for Chronic Obstructive Pulmonary Disease (COPD). We identified patients aged ≥40 years with unp...

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

Radiology
Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast CT provides airway and parenchymal measurements but cannot be used to directly determine lung funct...