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

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. Methods: The CAIDE study was based on previous population-based midlife...

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Autores principales: Pekkala, Timo, Hall, Anette, Lötjönen, Jyrki, Mattila, Jussi, Soininen, Hilkka, Ngandu, Tiia, Laatikainen, Tiina, Kivipelto, Miia, Solomon, Alina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5147511/
https://www.ncbi.nlm.nih.gov/pubmed/27802228
http://dx.doi.org/10.3233/JAD-160560
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author Pekkala, Timo
Hall, Anette
Lötjönen, Jyrki
Mattila, Jussi
Soininen, Hilkka
Ngandu, Tiia
Laatikainen, Tiina
Kivipelto, Miia
Solomon, Alina
author_facet Pekkala, Timo
Hall, Anette
Lötjönen, Jyrki
Mattila, Jussi
Soininen, Hilkka
Ngandu, Tiia
Laatikainen, Tiina
Kivipelto, Miia
Solomon, Alina
author_sort Pekkala, Timo
collection PubMed
description 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. Methods: The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). Results: AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. Conclusion: The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.
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spelling pubmed-51475112016-12-12 Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study Pekkala, Timo Hall, Anette Lötjönen, Jyrki Mattila, Jussi Soininen, Hilkka Ngandu, Tiia Laatikainen, Tiina Kivipelto, Miia Solomon, Alina J Alzheimers Dis Research Article 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. Methods: The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). Results: AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. Conclusion: The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions. IOS Press 2016-12-06 /pmc/articles/PMC5147511/ /pubmed/27802228 http://dx.doi.org/10.3233/JAD-160560 Text en IOS Press and the authors. All rights reserved https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Pekkala, Timo
Hall, Anette
Lötjönen, Jyrki
Mattila, Jussi
Soininen, Hilkka
Ngandu, Tiia
Laatikainen, Tiina
Kivipelto, Miia
Solomon, Alina
Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study
title Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study
title_full Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study
title_fullStr Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study
title_full_unstemmed Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study
title_short Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study
title_sort development of a late-life dementia prediction index with supervised machine learning in the population-based caide study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5147511/
https://www.ncbi.nlm.nih.gov/pubmed/27802228
http://dx.doi.org/10.3233/JAD-160560
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