AIMC Topic: Finland

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Deep learning-based prediction of one-year mortality in Finland is an accurate but unfair aging marker.

Nature aging
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
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)
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
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 ...

Attitudes Toward the Adoption of Remote Patient Monitoring and Artificial Intelligence in Parkinson's Disease Management: Perspectives of Patients and Neurologists.

The patient
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
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)
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.

Semiautomatic Assessment of Facet Tropism From Lumbar Spine MRI Using Deep Learning: A Northern Finland Birth Cohort Study.

Spine
STUDY DESIGN: This is a retrospective, cross-sectional, population-based study that automatically measured the facet joint (FJ) angles from T2-weighted axial magnetic resonance imagings (MRIs) of the lumbar spine using deep learning (DL).

Developing a model to explain users' ethical perceptions regarding the use of care robots in home care: A cross-sectional study in Ireland, Finland, and Japan.

Archives of gerontology and geriatrics
To date, research on ethical issues regarding care robots for older adults, family caregivers, and care workers has not progressed sufficiently. This study aimed to build a model that universally explains the relationship between the use of care robo...