Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.

Journal: Journal of Alzheimer's disease : JAD

Published Date: Jan 1, 2017

Abstract

BACKGROUND AND OBJECTIVE: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study.

Authors

Timo Pekkala

Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland.
Anette Hall

Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland.
Jyrki Lötjönen

VTT Technical Research Centre of Finland, Tampere, Finland.
Jussi Mattila

Combinostics, Tampere, Finland.
Hilkka Soininen

Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland.
Tiia Ngandu

Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland.
Tiina Laatikainen

Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland.
Miia Kivipelto

Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland.
Alina Solomon

Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland.

Keywords

Aged Apolipoproteins E Cerebrovascular Disorders Cognition Community Health Planning Dementia Female Finland Humans Male Neuropsychological Tests Predictive Value of Tests Reproducibility of Results Retrospective Studies Risk Factors ROC Curve Severity of Illness Index Supervised Machine Learning

External Resources

View on PubMed Access via DOI PubMed (27802228)

Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.

Abstract

Authors

Keywords

External Resources

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