Pulmonology

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

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Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data.

Currently available risk prediction methods are limited in their ability to deal with complex, heter...

Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination.

Lung auscultation is an important part of a physical examination. However, its biggest drawback is i...

Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach.

BACKGROUND: Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are unde...

Pulmonary nodule detection in CT scans with equivariant CNNs.

Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which i...

Feature-weighted survival learning machine for COPD failure prediction.

Chronic obstructive pulmonary disease (COPD) yields a high rate of failures such as hospital readmis...

Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.

Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the ...

Machine learning for patient risk stratification for acute respiratory distress syndrome.

BACKGROUND: Existing prediction models for acute respiratory distress syndrome (ARDS) require manual...

An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare.

This study proposes a convolutional neural network model trained from scratch to classify and detect...

Application of deep learning-based computer-aided detection system: detecting pneumothorax on chest radiograph after biopsy.

OBJECTIVES: To retrospectively evaluate the diagnostic performance of a convolutional neural network...

Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals.

Sleep is a prominent physiological activity in our daily life. Sleep apnea is the category of sleep ...

Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks.

Deep learning techniques have been extensively used in computerized pulmonary nodule analysis in rec...

LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features.

BACKGROUND: Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing stu...

Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma.

PURPOSE: To identify the role of radiomics texture features both within and outside the nodule in pr...

Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes.

As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficu...

Expert-level classification of ventilatory thresholds from cardiopulmonary exercising test data with recurrent neural networks.

First and second ventilatory thresholds (VT and VT) represent the boundaries of the moderate-heavy a...

Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection.

Lung cancer is a global and dangerous disease, and its early detection is crucial for reducing the r...

Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases.

UNLABELLED: Computational Intelligence Re-meets Medical Image Processing A Comparison of Some Nature...

netDx: interpretable patient classification using integrated patient similarity networks.

Patient classification has widespread biomedical and clinical applications, including diagnosis, pro...

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