Pulmonology

Asthma

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

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Showing 561-580 of 2,161 articles

CLINICAL VALIDATION OF SWAASA ARTIFICIAL INTELLIGENCE PLATFORM USING COUGH SOUNDS FOR SCREENING AND DIAGNOSIS OF RESPIRATORY DISEASES

Analysis of cough sounds have the potential to give a clue regarding the underlying respiratory disease. The Swaasa AI platform using artificial intelligence technology can analyze the cough sounds and provides an output using cough as a marker. The Swaasa AI platform comes under the category of Software As a Medical Device (SaMD), the device can detect underlying respiratory condition as normal v...

Quantification of Optical Coherence Tomography Features in >3500 Patients with Inherited Retinal Disease Reveals Novel Genotype-Phenotype Associations

To quantify spectral-domain optical coherence tomography (SD-OCT) images cross-sectionally and longitudinally in a large cohort of molecularly characterized patients with inherited retinal disease (IRDs) from the UK. Retrospective study of imaging data. Patients with a clinical and molecularly confirmed diagnosis of IRD who have undergone macular SD-OCT imaging at Moorfields Eye Hospital (MEH) bet...

Identifying Key Predictive Features for Opioid Use Disorder Using Machine Learning

Opioid Use Disorder (OUD) continues to pose a pressing public health challenge across the United States, highlighting the critical need for early and ...

Epigenetic patient stratification reveals a sub-endotype of type 2 asthma with altered B-cell response

Despite biomarker-guided treatment strategies, clinical outcomes among patients with type 2 (T2)-high asthma remain heterogeneous, with some patients ...

How do clinician and parent reported data differ? An analysis of similarity and difference in the datasets from a cross-syndrome genetics cohort study(GenROC)

Parent/patient-reported datasets provide ready access to phenotypic data for monogenic neurodevelopmental disorders yet their concordance with clinica...

Wearable Sleep Measures May Improve Machine Learning Prediction of Home-based Pulmonary Rehabilitation Engagement Among Patients With Chronic Obstructive Pulmonary Disease: A Proof-of-Concept Study

To evaluate whether incorporating baseline sleep measures from a wrist-worn activity monitor in machine learning (ML) models improved the prediction o...

Scaling genetic discovery for organ volumes using machine learning-assisted imputation and bias-corrected GWAS

MRI-derived organ and tissue volumes are powerful endophenotypes for studying complex disease, but their availability is limited by cost and throughpu...

The Cleaning Simulation: Applying Predictive Decision Trees for Chemical Exposure Risks and Asthma-Like Symptoms in Laboratory Workers

Exposure to chemical irritants in laboratory and medical environments poses significant health risks to workers, particularly in relation to asthma-li...

Development and evaluation of a multivariate prediction model for diagnosing asthma in patients with clinically suspected asthma using capnography

Diagnosis of asthma in primary care is challenged by a multistep pathway with variable adherence leading to significant misdiagnosis, late diagnosis a...

Identification of potential diagnostic markers and molecular mechanisms of asthma and ulcerative colitis based on bioinformatics and machine learning.

BACKGROUNDS: Asthma and ulcerative colitis (UC) are chronic inflammatory diseases linked through the "gut-lung axis," but their shared mechanisms rema...

Jan 1 2025 40443529
Home spirometry telemonitoring in pediatric patients with asthma: a mixed study.

BACKGROUND: To evaluate the feasibility and practicality of home spirometry telemonitoring for pediatric patients with asthma, including both motivato...

Jan 1 2025 40438788
A Comparative Study on Machine Learning Models to Classify Diseases Based on Patient Behaviour and Habits

In recent years, ML algorithms have been shown to be useful for predicting diseases based on health data and posed a potential application area for ...

Continual Learning Using a Kernel-Based Method Over Foundation Models

Continual learning (CL) learns a sequence of tasks incrementally. This paper studies the challenging CL setting of class-incremental learning (CIL)....

In-context learning for medical image segmentation

Annotation of medical images, such as MRI and CT scans, is crucial for evaluating treatment efficacy and planning radiotherapy. However, the extensi...

Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature

Section identification is an important task for library science, especially knowledge management. Identifying the sections of a paper would help fil...

Personalized and Safe Route Planning for Asthma Patients Using Real-Time Environmental Data

Asthmatic patients are very frequently affected by the quality of air, climatic conditions, and traffic density during outdoor activities. Most of t...

Atrial Fibrillation Detection System via Acoustic Sensing for Mobile Phones

Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria, which can lead to severe complications and even...

MixEHR-Nest: Identifying Subphenotypes within Electronic Health Records through Hierarchical Guided-Topic Modeling

Automatic subphenotyping from electronic health records (EHRs)provides numerous opportunities to understand diseases with unique subgroups and enhan...

Effect of Clinical History on Predictive Model Performance for Renal Complications of Diabetes

Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy, which, in turn, is the leading cause of end-stage chr...

Enhancing Asthma Self-Management with Environmental Passive-Monitoring Data and Machine Learning-Based Predictions.

Monitoring enables timely action which is critical in avoiding asthma attacks. With the abundance of local weather and pollution data, when augmented ...

Aug 22 2024 39176891
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