AIMC Topic: Finland
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Predicting Alzheimer's disease from cognitive footprints in mid and late life: How much can register data and machine learning help?
International journal of medical informatics
Jul 3, 2024
BACKGROUND: Real-world data with decades-long medical records are increasingly available alongside the growing adoption of machine learning in healthcare research. We evaluated the performance of machine learning models in predicting the risk of Alzh...
Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset.
PloS one
Jun 25, 2024
Cephalometric analysis is critically important and common procedure prior to orthodontic treatment and orthognathic surgery. Recently, deep learning approaches have been proposed for automatic 3D cephalometric analysis based on landmarking from CBCT ...
Deep learning-based prediction of one-year mortality in Finland is an accurate but unfair aging marker.
Nature aging
Jun 24, 2024
Short-term mortality risk, which is indicative of individual frailty, serves as a marker for aging. Previous age clocks focused on predicting either chronological age or longer-term mortality. Aging clocks predicting short-term mortality are lacking ...
Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.
Frontiers in public health
May 28, 2024
INTRODUCTION: Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to p...
Incorporation of water quality index models with machine learning-based techniques for real-time assessment of aquatic ecosystems.
Environmental pollution (Barking, Essex : 1987)
May 27, 2024
Water quality index (WQI) is a well-established tool for assessing the overall quality of fresh inland-waters. However, the effectiveness of real-time assessment of aquatic ecosystems using the WQI is usually impacted by the absence of some water qua...
Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data.
BMC medical informatics and decision making
May 2, 2024
BACKGROUND: Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little ...
Prognostic value of a novel artificial intelligence-based coronary CTA-derived ischemia algorithm among patients with normal or abnormal myocardial perfusion.
Journal of cardiovascular computed tomography
Apr 24, 2024
BACKGROUND: Among patients with obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CTA), downstream positron emission tomography (PET) perfusion imaging can be performed to assess the presence of myocardial ischemi...
Finland
Angina, Unstable
Coronary Stenosis
Adenosine
Predictive Value of Tests
Coronary Angiography
Coronary Artery Disease
Positron-Emission Tomography
Coronary Vessels
Vasodilator Agents
Computed Tomography Angiography
Humans
Aged
Coronary Circulation
Artificial Intelligence
Myocardial Perfusion Imaging
Algorithms
Retrospective Studies
Male
Prognosis
Female
Middle Aged
Reproducibility of Results
Severity of Illness Index
Risk Factors
Time Factors
Attitudes Toward the Adoption of Remote Patient Monitoring and Artificial Intelligence in Parkinson's Disease Management: Perspectives of Patients and Neurologists.
The patient
Jan 5, 2024
OBJECTIVE: Early detection of Parkinson's Disease (PD) progression remains a challenge. As remote patient monitoring solutions (RMS) and artificial intelligence (AI) technologies emerge as potential aids for PD management, there's a gap in understand...
Risk adjustment for regional healthcare funding allocations with ensemble methods: an empirical study and interpretation.
The European journal of health economics : HEPAC : health economics in prevention and care
Jan 3, 2024
We experiment with recent ensemble machine learning methods in estimating healthcare costs, utilizing Finnish data containing rich individual-level information on healthcare costs, socioeconomic status and diagnostic data from multiple registries. Ou...
Transfer learning for the generalization of artificial intelligence in breast cancer detection: a case-control study.
Acta radiologica (Stockholm, Sweden : 1987)
Dec 19, 2023
BACKGROUND: Some researchers have questioned whether artificial intelligence (AI) systems maintain their performance when used for women from populations not considered during the development of the system.
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