Pulmonology

Provenance for Biomedical Ontologies with RDF and Git.

The German Center for Lung Research (DZL) is a research network with the aim of researching respiratory diseases. In order to enable consortium-wide retrospective research and prospective patient recruitment, we perform data integration into a central data warehouse. The enhancements of the underlying ontology is an ongoing process for which we developed the Collaborative Metadata Repository (CoMe...

Machine-Learning and Stochastic Tumor Growth Models for Predicting Outcomes in Patients With Advanced Non-Small-Cell Lung Cancer.

PURPOSE: The prediction of clinical outcomes for patients with cancer is central to precision medicine and the design of clinical trials. We developed and validated machine-learning models for three important clinical end points in patients with advanced non-small-cell lung cancer (NSCLC)-objective response (OR), progression-free survival (PFS), and overall survival (OS)-using routinely collected ...

A new and efficient numerical method for the fractional modeling and optimal control of diabetes and tuberculosis co-existence.

The main objective of this research is to investigate a new fractional mathematical model involving a nonsingular derivative operator to discuss the c...

Of Slide Rules and Stethoscopes: AI and the Future of Doctoring.

Historically, the practice of medicine has been a physically intimate endeavor. Physicians have used their hands to palpate and reveal the secrets hid...

[Research progress on computed tomography image detection and classification of pulmonary nodule based on deep learning].

Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the su...

Predicting Disease-Free Lung Cancer Survival Using Patient Reported Outcome (PRO) Measurements with Comparisons of Five Machine Learning Techniques (MLT).

The study was to develop the lung cancer patients' prediction model for predicting 5-year survival after completion of treatment by using Machine Lear...

Development and Validation of a Deep Learning-based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs.

BACKGROUND: Detection of active pulmonary tuberculosis on chest radiographs (CRs) is critical for the diagnosis and screening of tuberculosis. An auto...

[Research progress of the application of AI in the diagnosis and treatment of respiratory disease].

Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning.

Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-i...

Classification of radiographic lung pattern based on texture analysis and machine learning.

This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions...

Prediction of effluent quality in ICEAS-sequential batch reactor using feedforward artificial neural network.

It is highly essential that municipal wastewater is treated before its discharge and reuse in order to meet the standard requirements for safe marine ...

Deep Q-learning for Predicting Asthma Attack with Considering Personalized Environmental Triggers' Risk Scores.

The purpose of our present study was to develop a forecasting method that would help asthmatic individuals to take evasive action when the probability...

Sleep Apnea Severity Estimation from Respiratory Related Movements Using Deep Learning.

Sleep apnea is a common chronic respiratory disorder which occurs due to the repetitive complete or partial cessations of breathing during sleep. The ...

Applying Machine Learning Algorithms for Automatic Detection of Swallowing from Sound.

Despite the severe consequences of dysfunctional swallowing, there is no simple method of monitoring swallowing outside of clinical settings. People w...

A Comparative Study of Features for Acoustic Cough Detection Using Deep Architectures.

Automatic cough detection is key to tracking the condition of patients suffering from tuberculosis. We evaluate various acoustic features for performi...

A Comparison of SVM and CNN-LSTM Based Approach for Detecting Smoke Inhalations from Respiratory signal.

Wearable sensors have successfully been used in recent studies to monitor cigarette smoking events and analyze people's smoking behavior. Respiratory ...

Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs.

Respiratory diseases account for a significant proportion of deaths and disabilities across the world. Chest X-ray (CXR) analysis remains a common dia...

Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset.

The use of ImageNet pre-trained networks is becoming widespread in the medical imaging community. It enables training on small datasets, commonly avai...

Reckoning respiratory signals to affectively decipher mental state.

Recognizing mental states from physiological signal is a concern not only for medical diagnostics, but also for cognitive science, behavioral studies ...

Attention-Guided Convolutional Neural Network for Detecting Pneumonia on Chest X-Rays.

Pneumonia is a common infectious disease in the world. Its main diagnostic method is chest X-ray (CXR) examination. However, the high visual similarit...