# Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/unsupervised-machine-learning-to-identify-high-likelihood-of-dementia-in/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Laurent Cléret de Langavant,É. Bayen,Kristine Yaffe |
| Citations | 83 |
| DOI | 10.2196/10493 |
| Fields | Biochemistry, Genetics and Molecular Biology,Health Professions,Medicine |
| Open Access | true |
| OA Status | gold |
| OA URL | https://doi.org/10.2196/10493 |
| OpenAlex ID | https://openalex.org/W2801132216 |
| PMID | 29986849 |
| Type | article |
| Year | 2018 |

## Paper authors

- [É. Bayen](https://scholariq.org/researchers/e-bayen/)

## Paper journal

- [Journal of Medical Internet Research](https://scholariq.org/journals/journal-of-medical-internet-research/)

## Paper primary topic

- [Dementia and Cognitive Impairment Research](https://scholariq.org/topics/dementia-and-cognitive-impairment-research/)

## Paper topics

- [Dementia and Cognitive Impairment Research](https://scholariq.org/topics/dementia-and-cognitive-impairment-research/)
- [Biological Research and Disease Studies](https://scholariq.org/topics/biological-research-and-disease-studies/)
- [Older Adults Driving Studies](https://scholariq.org/topics/older-adults-driving-studies/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.