Pulmonology

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

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Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.

Viruses are widely recognized to be intricately associated with both solid and hematological maligna...

Predicting the risk of pulmonary infection after kidney transplantation using machine learning methods: a retrospective cohort study.

PURPOSE: Pulmonary infection is the most common and serious complication after kidney transplantatio...

Prediction of the risk of mortality in older patients with coronavirus disease 2019 using blood markers and machine learning.

INTRODUCTION: The mortality rate among older people infected with severe acute respiratory syndrome ...

Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters.

PURPOSE: External physiological monitoring is the primary approach to measure and remove effects of ...

Quantitative analysis of imaging characteristics in lung adenocarcinoma in situ using artificial intelligence.

BACKGROUND: With the rising incidence of pulmonary nodules (PNs), lung adenocarcinoma in situ (AIS) ...

Metabolomics-Based Machine Learning for Predicting Mortality: Unveiling Multisystem Impacts on Health.

Reliable predictors of long-term all-cause mortality are needed for middle-aged and older population...

Real-World Performance of Pneumothorax-Detecting Artificial Intelligence Algorithm and its Impact on Radiologist Reporting Times.

RATIONALE AND OBJECTIVES: Artificial intelligence (AI) algorithms in radiology capable of detecting ...

How AI Could Help Us in the Epidemiology and Diagnosis of Acute Respiratory Infections?

Acute respiratory infections (ARIs) represent a significant global health burden, contributing to hi...

Optimizing lipid nanoparticles for fetal gene delivery in vitro, ex vivo, and aided with machine learning.

There is a clinical need to develop lipid nanoparticles (LNPs) to deliver congenital therapies to th...

Anomaly detection scheme for lung CT images using vector quantized variational auto-encoder with support vector data description.

This study aims to develop an anomaly-detection scheme for lesions in CT images. Our database consis...

Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

PURPOSE: Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited...

Enabling machine learning models in alarm fatigue research: Creation of a large relevance-annotated oxygen saturation alarm data set.

BACKGROUND: Too many unnecessary alarms in the intensive care unit are one of the main reasons for a...

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