AIMC Topic: Scotland
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Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.
NPJ primary care respiratory medicine
Apr 23, 2025
Primary care consultations provide an opportunity for patients and clinicians to assess asthma attack risk. Using a data-driven risk prediction tool with routinely collected health records may be an efficient way to aid promotion of effective self-ma...
RCPE in association with the American College of Gastroenterology and the Scottish Society of Gastroenterology - Gastroenterology: A global perspective.
The journal of the Royal College of Physicians of Edinburgh
Apr 12, 2025
On 6 November 2024, the Royal College of Physicians of Edinburgh (RCPE) hosted its annual gastroenterology symposium, marking the first collaboration with the American College of Gastroenterology (ACG) and the Scottish Society of Gastroenterology (SS...
Machine learning-based models for prediction of innovative medicine reimbursement decisions in Scotland.
Journal of epidemiology and population health
Jan 17, 2025
OBJECTIVE: This study aimed to investigate the critical factors for reimbursement decisions of innovative medicines in Scotland and to explore the feasibility of machine learning models for predicting decisions.
Care home resident identification: A comparison of address matching methods with Natural Language Processing.
PloS one
Dec 5, 2024
BACKGROUND: Care home residents are a highly vulnerable group, but identifying care home residents in routine data is challenging. This study aimed to develop and validate Natural Language Processing (NLP) methods to identify care home residents from...
Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland.
International journal of environmental research and public health
Jun 27, 2024
The total fertility rate is influenced over an extended period of time by shifts in population socioeconomic characteristics and attitudes and values. However, it may be impacted by macroeconomic trends in the short term, although these effects are l...
Deep learning detection of diabetic retinopathy in Scotland's diabetic eye screening programme.
The British journal of ophthalmology
Jun 20, 2024
BACKGROUND/AIMS: Support vector machine-based automated grading (known as iGradingM) has been shown to be safe, cost-effective and robust in the diabetic retinopathy (DR) screening (DES) programme in Scotland. It triages screening episodes as gradabl...
Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme.
The British journal of ophthalmology
May 21, 2024
BACKGROUND/AIMS: National guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on images to predict progression to ref...
Artificial intelligence-assisted automated heart failure detection and classification from electronic health records.
ESC heart failure
May 3, 2024
AIMS: Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. ...
A stakeholder analysis to prepare for real-world evaluation of integrating artificial intelligent algorithms into breast screening (PREP-AIR study): a qualitative study using the WHO guide.
BMC health services research
May 2, 2024
BACKGROUND: The national breast screening programme in the United Kingdom is under pressure due to workforce shortages and having been paused during the COVID-19 pandemic. Artificial intelligence has the potential to transform how healthcare is deliv...
Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes? A prospective cohort study from the national screening programme in Scotland.
International journal of medical informatics
Apr 18, 2023
AIMS: This study's objective was to evaluate whether deep learning (DL) on retinal photographs from a diabetic retinopathy screening programme improve prediction of incident cardiovascular disease (CVD).
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