COPD

Identifying anaphylaxis using weakly-supervised prediction models and natural language processing

Objectives Scalable computable phenotyping algorithms are critical for conducting high-throughput disease-outcome research in large, distributed-data electronic health record (EHR) and claims data settings. We developed and evaluated a claims- and EHR-based computable phenotyping algorithm for anaphylaxis, a rare acute condition that is challenging to accurately identify using claims data alone. M...

Artificial Intelligence-Based Detection of Airway Mucus Plugs on CT and Associations With Clinical Outcomes in COPDGene

RATIONALE: Airway mucus plugging is a clinically relevant manifestation of airway pathology in chronic obstructive pulmonary disease (COPD) and is associated with increased mortality even in early disease; however, visual computed tomography (CT) assessment is subjective and labor intensive. OBJECTIVES: To develop an AI-based quantitative CT method for automated detection of airway mucus plugging ...

Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering

Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditionin...

General-purpose large language models can achieve physician-level accuracy in complex medical data extraction

Background: Unstructured data represent about 80% of total electronic health records (EHR) data. Structuring this free text is essential for advancing...

End-to-End Machine Learning for Depressive State Classification via EEG and fNIRS

The escalating demand for mental healthcare, driven by rising societal stress, highlights the limitations of traditional psychiatric diagnostics. Conv...

Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

This study aims to explore the performance of the VAR model in comparison with mel-frequency cepstral coefficient (MFCC) matrices and log-mel spectrog...

Predicting the timing of first sustained cognitive worsening in Alzheimer's disease using real-world clinical data and machine learning

Background: Cognitive assessments are sparsely documented in electronic health records (EHRs), limiting scalable detection of cognitive worsening in r...

Interpretable machine learning for coeliac disease diagnosis: quantitative morphometry of duodenal biopsies

Background Coeliac disease affects approximately 1% of the global population and remains substantially underdiagnosed. Histopathological assessment of...

Frontier Large Language Models for Comprehensive Medication Review in CKD Patients with Polypharmacy: A Trap-Embedded Synthetic Benchmark

Background: Patients with CKD and polypharmacy face high rates of drug-related problems, yet comprehensive medication review remains time-intensive an...

Computational Design of Novel Selective Phosphodiesterase 4B Inhibitors from Natural Products: An Integrated Machine Learning and Structure-Based Drug Discovery Approach

Abstract Selective inhibition of phosphodiesterase 4B (PDE4B) remains a promising strategy for preserving the anti-inflammatory benefit of PDE4 inhibi...

Angel or Demon: Investigating the Plasticity Interventions' Impact on Backdoor Threats in Deep Reinforcement Learning

Extensive research has highlighted the severe threats posed by backdoor attacks to deep reinforcement learning (DRL). However, prior studies primarily...

Estimation of Physiological Metrics from Resting ECGs Using Deep Learning in the UK Biobank, Including submaximal exercise derived VO2max, Body Fat Percentage, and Grip Strength

Maximal oxygen consumption VO2max is the gold standard for cardiorespiratory fitness but requires resource-intensive physical testing. Recent reports ...

Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation

Multi-window CT imaging captures complementary pathological information across anatomical structures of differing densities, yet existing deep learnin...

Eliciting associations between clinical variables from LLMs via comparison questions across populations

The training data of large language models (LLMs) comprises a wide range of biomedical literature, reflecting data from many different patient populat...

Hierarchical integration of multimodal clinical data to predict epilepsy surgery outcome

Background: Integrating multimodal data into medical artificial intelligence (AI) tools and evaluating whether they outperform human experts remains a...

Co-Evolving Policy Distillation

RLVR and OPD have become standard paradigms for post-training. We provide a unified analysis of these two paradigms in consolidating multiple expert c...

Detection of bronchopulmonary dysplasia in infants and prediction of school-age lung function from tidal breathing data using recurrent neural networks

Objective: To test whether machine learning (ML) models trained on tidal breathing flow time series can discriminate between individuals with and with...

Feature-Based Parametric Response Mapping on Thoracic Computed Tomography for Robust Disease Classification in COPD

Purpose: To develop an interpretable feature-based Deep Parametric Response Mapping (PRMD) method that combines wavelet scattering convolution network...

Testing and Estimating Causal Treatment Effect Heterogeneity in Observational Studies via Revised Deep Semiparametric Regression: A Lung Transplant Case Study

Lung transplantation programs must decide when bilateral lung transplantation (BLT) offers meaningful functional benefit over single lung transplantat...

BioUNER: A Benchmark Dataset for Clinical Urdu Named Entity Recognition

In this article, we present a gold-standard benchmark dataset for Biomedical Urdu Named Entity Recognition (BioUNER), developed by crawling health-rel...