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Identification of heart failure subtypes using transformer-based deep learning modelling: a population-based study of 379,108 individuals.
EBioMedicine
Mar 19, 2025
BACKGROUND: Heart failure (HF) is a complex syndrome with varied presentations and progression patterns. Traditional classification systems based on left ventricular ejection fraction (LVEF) have limitations in capturing the heterogeneity of HF. We a...
Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning.
Scientific reports
Mar 18, 2025
Aneurysmal subarachnoid haemorrhage (aSAH) is a type of stroke with high mortality and morbidity. This study aimed to identify novel aSAH risk factors by combining machine learning (ML) and traditional statistical methods. Using the UK Biobank, we id...
Exploring the application of deep learning methods for polygenic risk score estimation.
Biomedical physics & engineering express
Mar 13, 2025
. Polygenic risk scores (PRS) summarise genetic information into a single number with clinical and research uses. Deep learning (DL) has revolutionised multiple fields, however, the impact of DL on PRSs has been less significant. We explore how DL ca...
Modifiable risk factors of vaccine hesitancy: insights from a mixed methods multiple population study combining machine learning and thematic analysis during the COVID-19 pandemic.
BMC medicine
Mar 12, 2025
BACKGROUND: Vaccine hesitancy, the delay in acceptance or reluctance to vaccinate, ranks among the top threats to global health. Identifying modifiable factors contributing to vaccine hesitancy is crucial for developing targeted interventions to incr...
Generative AI-Enabled Therapy Support Tool for Improved Clinical Outcomes and Patient Engagement in Group Therapy: Real-World Observational Study.
Journal of medical Internet research
Mar 10, 2025
BACKGROUND: Cognitive behavioral therapy (CBT) is a highly effective treatment for depression and anxiety disorders. Nonetheless, a substantial proportion of patients do not respond to treatment. The lack of engagement with therapeutic materials and ...
Addressing underestimation and explanation of retinal fundus photo-based cardiovascular disease risk score: Algorithm development and validation.
Computers in biology and medicine
Mar 9, 2025
OBJECTIVE: To resolve the underestimation problem and investigate the mechanism of the AI model which employed to predict cardiovascular disease (CVD) risk scores from retinal fundus photos.
An interpretable machine learning approach for detecting psoriatic arthritis in a UK primary care psoriasis cohort using electronic health records from the Clinical Practice Research Datalink.
Annals of the rheumatic diseases
Mar 1, 2025
OBJECTIVES: Develop an interpretable machine learning model to detect patients with newly diagnosed psoriatic arthritis (PsA) in a cohort of psoriasis patients and identify important clinical indicators of PsA in primary care.
Estimation of Machine Learning-Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study.
JMIR aging
Feb 26, 2025
BACKGROUND: The atherosclerotic cardiovascular disease (ASCVD) is associated with dementia. However, the risk factors of dementia in patients with ASCVD remain unclear, necessitating the development of accurate prediction models.
Histological proven AI performance in the UKLS CT lung cancer screening study: Potential for workload reduction.
European journal of cancer (Oxford, England : 1990)
Feb 25, 2025
PURPOSE: Artificial intelligence (AI) could reduce lung cancer screening computer tomography (CT)-reading workload if used as a first-reader, ruling-out negative CT-scans at baseline. Evidence is lacking to support AI performance when compared to gol...
Understanding the Engagement and Interaction of Superusers and Regular Users in UK Respiratory Online Health Communities: Deep Learning-Based Sentiment Analysis.
Journal of medical Internet research
Feb 13, 2025
BACKGROUND: Online health communities (OHCs) enable people with long-term conditions (LTCs) to exchange peer self-management experiential information, advice, and support. Engagement of "superusers," that is, highly active users, plays a key role in ...
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