Development of a late-life dementia prediction index with supervised machine learning in the population-based CAIDE study
Journal of Alzheimer's Disease, ISSN: 1875-8908, Vol: 55, Issue: 3, Page: 1055-1067
2017
- 30Citations
- 131Captures
- 4Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations30
- Citation Indexes30
- 30
- CrossRef27
- Captures131
- Readers131
- 131
- Mentions4
- News Mentions4
- News4
Most Recent News
New tool to help predict dementia risk in older people
Preventing dementia is a major public health priority worldwide, and intense work is being conducted to formulate effective preventive strategies. Healthy lifestyle changes may help prevent cognitive decline and dementia, but the challenge is to detect early on those who are most at risk and to choose the most relevant preventive measures.
Article 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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85005977748&origin=inward; http://dx.doi.org/10.3233/jad-160560; http://www.ncbi.nlm.nih.gov/pubmed/27802228; http://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JAD-160560; https://journals.sagepub.com/doi/full/10.3233/JAD-160560; https://dx.doi.org/10.3233/jad-160560; https://content.iospress.com:443/articles/journal-of-alzheimers-disease/jad160560
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