Pulmonology

Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data.

OBJECTIVES: In lung transplantation (LTx), a priority is assigned to each candidate on the waiting list. Our primary objective was to identify the key factors that influence the allocation of priorities in LTx using machine learning (ML) techniques to enhance the process of prioritising patients.

Metabolomics and machine learning identify urine metabolic characteristics and potential biomarkers for severe Mycoplasma pneumoniae pneumonia.

To study the differences in the urine metabolome between pediatric patients with severe Mycoplasma pneumoniae pneumonia (SMPP) and those with general Mycoplasma pneumoniae pneumonia (GMPP) via non-targeted metabolomics method, and potential biomarkers were explored through machine learning (ML) algorithms. The urine metabonomics data of 48 children with SMPP and 85 children with GMPP were collecte...

Multicenter development of a deep learning radiomics and dosiomics nomogram to predict radiation pneumonia risk in non-small cell lung cancer.

Radiation pneumonia (RP) is the most common side effect of chest radiotherapy, and can affect patients' quality of life. This study aimed to establish...

Patient-specific uncertainty calibration of deep learning-based autosegmentation networks for adaptive MRI-guided lung radiotherapy.

Uncertainty assessment of deep learning autosegmentation (DLAS) models can support contour corrections in adaptive radiotherapy (ART), e.g. by utilizi...

Predicting Risk of Pulmonary Fibrosis Formation in PASC Patients

While the acute phase of the COVID-19 pandemic has subsided, its long-term effects persist through Post-Acute Sequelae of COVID-19 (PASC), commonly ...

LizAI XT -- Artificial Intelligence-Powered Platform for Healthcare Data Management: A Study on Clinical Data Mega-Structure, Semantic Search, and Insights of Sixteen Diseases

AI-powered LizAI XT ensures real-time and accurate mega-structure of different clinical datasets and largely inaccessible and fragmented sources, in...

An AI-driven framework for the prediction of personalised health response to air pollution

Air pollution poses a significant threat to public health, causing or exacerbating many respiratory and cardiovascular diseases. In addition, climat...

GRU-D Characterizes Age-Specific Temporal Missingness in MIMIC-IV.

Temporal missingness, defined as unobserved patterns in time series, and its predictive potentials represent an emerging area in clinical machine lear...

FLANDERS: Fast Learning COVID-19 Care System.

The COVID-19 pandemic highlighted the complexities of diagnosing and managing acute Respiratory Failure (RF). Early prediction of RF remains a key cha...

Federated Learning for Predictive Analytics in Weaning from Mechanical Ventilation.

Mechanical ventilation is crucial for critically ill patients in ICUs, requiring accurate weaning and extubations timing for optimal outcomes. Current...

Artificial Intelligence Powered Audiomics: The Futuristic Biomarker in Pulmonary Medicine - A State-of-the-Art Review.

AI-driven "audiomics" leverages voice and respiratory sounds as non-invasive biomarkers to diagnose and manage pulmonary conditions, including COVID-1...

A Knowledge Graph for the Exploration of Human RSV Infection.

Despite decades of research and disease burden, RSV remains the most common cause of acute lower respiratory tract infection (ALRTI) in infants <2 yea...

Deep Learning-Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a common and progressive respiratory condition characterized by persistent airflow limitat...

Evaluation of a Machine Learning Model Based on Laboratory Parameters for the Prediction of Influenza A and B in Chongqing, China: Multicenter Model Development and Validation Study.

BACKGROUND: Influenza viruses are major pathogens responsible for acute respiratory infections in humans, which present with symptoms such as fever, c...

Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.

Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant challenges to public health. Among its various complications, diabetic n...

A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS.

BACKGROUND: Depression associated with Chronic Obstructive Pulmonary Disease (COPD) is a detrimental complication that significantly impairs patients'...

Automated Detection of Clinical Entities in Lung and Breast Cancer Reports Using NLP Techniques

Research projects, including those focused on cancer, rely on the manual extraction of information from clinical reports. This process is time-consu...

Explainability Through Human-Centric Design for XAI in Lung Cancer Detection

Deep learning models have shown promise in lung pathology detection from chest X-rays, but widespread clinical adoption remains limited due to opaqu...

DCSNet: A Lightweight Knowledge Distillation-Based Model with Explainable AI for Lung Cancer Diagnosis from Histopathological Images

Lung cancer is a leading cause of cancer-related deaths globally, where early detection and accurate diagnosis are critical for improving survival r...

Development and Validation of a Machine Learning-Based Predictive Model for Postoperative Frailty in Patients with Non-Small Cell Lung Cancer and Its Relation to Early Recovery.

PURPOSE: This study was designed to evaluate the postoperative frailty status of patients with non-small cell lung cancer, identify influencing factor...