Pulmonology

Latest AI and machine learning research in pulmonology for healthcare professionals.

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Advancing lung transplantation through machine learning and artificial intelligence.

PURPOSE OF REVIEW: To explore the current applications of artificial intelligence and machine learni...

A machine learning model for predicting severe mycoplasma pneumoniae pneumonia in school-aged children.

OBJECTIVE: To develop an interpretable machine learning (ML) model for predicting severe Mycoplasma ...

A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset.

Tuberculosis, a deadly and contagious disease caused by Mycobacterium tuberculosis, remains a signif...

Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study.

Accurate assessment of training status in team sports is crucial for optimising performance and redu...

CAD-Unet: A capsule network-enhanced Unet architecture for accurate segmentation of COVID-19 lung infections from CT images.

Since the outbreak of the COVID-19 pandemic in 2019, medical imaging has emerged as a primary modali...

Advancing harmful algal bloom predictions using chlorophyll-a as an Indicator: Combining deep learning and EnKF data assimilation method.

The use of data driven deep learning models to predict and monitor Harmful Algal Blooms (HABs) has e...

Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors.

We hypothesized that incorporating postnatal dynamic factors would enhance the prediction accuracy o...

Utilizing artificial intelligence to predict and analyze socioeconomic, environmental, and healthcare factors driving tuberculosis globally.

Tuberculosis (TB) is a major global health issue, contributing significantly to mortality and morbid...

Explainable machine learning for predicting lung metastasis of colorectal cancer.

Patients with lung metastasis of colorectal cancer typically have a poor prognosis. Therefore, estab...

Deep learning unlocks the true potential of organ donation after circulatory death with accurate prediction of time-to-death.

Increasing the number of organ donations after circulatory death (DCD) has been identified as one of...

Habitat Radiomics and Deep Learning Features Based on CT for Predicting Lymphovascular Invasion in T1-stage Lung Adenocarcinoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: The research aims to examine how CT-derived habitat radiomics can be used ...

Updated perspectives on visceral pleural invasion in non-small cell lung cancer: A propensity score-matched analysis of the SEER database.

BACKGROUND: Visceral pleural invasion (VPI), including PL1 (the tumor invades beyond the elastic lay...

Diagnostic MicroRNA Signatures to Support Classification of Pulmonary Hypertension.

BACKGROUND: Patients with pulmonary hypertension (PH) are classified based on disease pathogenesis a...

Hierarchical embedding attention for overall survival prediction in lung cancer from unstructured EHRs.

The automated processing of Electronic Health Records (EHRs) poses a significant challenge due to th...

Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.

BACKGROUND: Pediatric asthma is a common chronic respiratory disease worldwide, and its acute exacer...

A machine learning-based severity stratification tool for high altitude pulmonary edema.

This study aimed to identify key predictors for the severity of High Altitude Pulmonary Edema (HAPE)...

Predicting mortality and risk factors of sepsis related ARDS using machine learning models.

Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic....

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