Atopy

Development and validation of a machine learning stratified prediction model for early warning of anti-tuberculosis drug-induced liver injury risk based on real-world data: a retrospective cohort study.

OBJECTIVE: This study aimed to develop and validate stratified machine learning models for early prediction of anti-tuberculosis drug-induced liver injury (ATB-DILI) risk, targeting both a general tuberculosis (TB) treatment population and a high-risk subgroup with chronic hepatitis B (CHB) co-infection, based on real-world clinical data. METHODS: A single-center retrospective cohort study was con...

[Imaging of eosinophilic pneumonia : Differential diagnosis in radiological practice].

CLINICAL/METHODICAL ISSUE: Eosinophilic pneumonias are rare inflammatory lung diseases with heterogeneous clinical presentation and variable computer tomography (CT) patterns. Overlap with infectious, drug-induced, or neoplastic entities poses a diagnostic challenge in radiological practice. STANDARD RADIOLOGICAL METHODS: High-resolution computed tomography (HRCT) is the key imaging modality. Typi...

Machine Learning Model Development and Validation in Individuals With Suggestive Hypersensitivity to Nonsteroidal Anti-inflammatory Drugs.

BACKGROUND AND OBJECTIVES: Nonsteroidal anti-inflammatory drugs (NSAIDs) are the main triggers of drug hypersensitivity reactions (HSRs). NSAID-induce...

Update on Non-Biological and RNA-Based Therapeutics in Chronic Inflammatory Diseases: Precision Medicine Through Small Molecules.

In the last decades, critical advancements in research technology and knowledge on disease mechanisms steered therapeutic approaches for chronic infla...

Xanthine oxidase inhibitors for gout: Applications and novel drug development.

Hyperuricemia is a well-established direct trigger of gouty arthritis. Xanthine oxidase (XO), the rate-limiting enzyme in uric acid production, repres...

Improving the management and outcomes of preschool wheeze: protocol of a prospective multicentre cohort study.

INTRODUCTION: Preschool wheeze and asthma are associated with substantial morbidity and impaired future lung function. Yet, wheeze is unreliably repor...

Endoscopic Diagnosis of Eosinophilic Esophagitis Using a Multi-Task U-Net: A Pilot Study.

PURPOSE: Endoscopically identifying eosinophilic esophagitis (EoE) is difficult due to its rare incidence and subtle morphology. We aimed to develop a...

Artificial Intelligence-derived Measurements of Myosteatosis from Coronary Artery Calcium CT Scans to Predict COPD: The Multi-Ethnic Study of Atherosclerosis.

Purpose To evaluate the predictive value of myosteatosis as an opportunistic finding in coronary artery calcium (CAC) CT scans for clinically diagnose...

Filling the Gaps in Health Data: Using a Machine Learning Approach to Augment Partially Observed Variables Such as Smoking in Claims Data.

PURPOSE: Missing information is common in real-world claims data, particularly on behavioral confounders, for example, smoking. Often one category of ...

CYP2C19 genotype-guided dosing of proton pump inhibitors: progress in clinical trials and real-world use.

INTRODUCTION: Proton pump inhibitors (PPIs) are widely used to treat acid-related disorders; however, treatment response varies significantly due to C...

Response to venom immunotherapy: Exploratory retrospective machine learning clustering analysis.

BACKGROUND: Hymenoptera venom immunotherapy is an established treatment for severe allergic reactions, aiming to modulate the immune response and redu...

Expanding role of cell-free DNA for the early diagnosis and monitoring of pulmonary diseases.

Circulating cell-free DNA (cfDNA) has firmly established itself as a cornerstone of liquid biopsy, advancing the noninvasive diagnosis and monitoring ...

Role of WNK4 and FKBP5 in asthma: Insights from bioinformatics analysis, machine learning, and preliminary validation.

BACKGROUND: The complex and heterogeneous molecular mechanisms of asthma are currently unclear, and treatment outcomes are dismal in some patients. Th...

Prediction of renal cell carcinoma: Development and validation of machine learning model.

Renal cell carcinoma (RCC) is the leading cause of urinary system morbidity and mortality. Early identification is crucial for improving RCC patient o...

Reproducing real-world clinical prediction models using the DIVE platform: A comparative validation study across three chronic diseases.

OBJECTIVES: The aim of this analysis is to evaluate the performance and reproducibility of the Python-based Data Insight Validation Engine (DIVE), a m...

Indoor air quality in primary schools: real-time monitoring and predictive modeling of PM10 in Kenitra, Morocco.

The health of children may be adversely influenced by the air quality in schools because they are more sensitive to indoor air pollutants. PM10, which...

Prediction model for cardiovascular mortality risk in asthma patients based on machine learning.

BackgroundAsthma is a common chronic respiratory disease, cardiovascular disease (CVD) mortality constitutes a major public health concern. At present...

Lightweight interpretable AI model using multiple blood parameters for emergency diagnosis of acute appendicitis.

BACKGROUND: Acute appendicitis poses diagnostic challenges due to symptom overlap with other abdominal conditions, often leading to misdiagnosis or mi...

TNFAIP3, PADI4, and CXCR4 as biomarkers of neutrophil extracellular traps in severe asthma.

Asthma is a heterogeneous condition impacting an estimated 300 million individuals globally. Although inhaled corticosteroids are effective in allevia...

Pediatric asthma management via integration of a remote spirometry device into an EHR-based artificial intelligence-powered clinical decision support system: A feasibility pragmatic clinical trial.

BACKGROUND: Asthma is the most common chronic disease in children. Suboptimal asthma control is prevalent and causes significant health care costs. El...