Cargando…

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

BACKGROUND: Dementia is increasing in prevalence worldwide, yet frequently remains undiagnosed, especially in low- and middle-income countries. Population-based surveys represent an underinvestigated source to identify individuals at risk of dementia. OBJECTIVE: The aim is to identify participants w...

Descripción completa

Detalles Bibliográficos
Autores principales:	Cleret de Langavant, Laurent, Bayen, Eleonore, Yaffe, Kristine
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	JMIR Publications 2018
Materias:	Original Paper
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6056741/ https://www.ncbi.nlm.nih.gov/pubmed/29986849 http://dx.doi.org/10.2196/10493

Ejemplares similares

Approximating dementia prevalence in population‐based surveys of aging worldwide: An unsupervised machine learning approach
por: Cleret de Langavant, Laurent, et al.
Publicado: (2020)

Identification of high likelihood of dementia in population-based surveys using unsupervised clustering: a longitudinal analysis
por: Gharbi-Meliani, Amin, et al.
Publicado: (2023)

Identification of High Likelihood of Dementia in Population-Based Surveys using Unsupervised Clustering: a Longitudinal Analysis
por: Gharbi-Meliani, Amin, et al.
Publicado: (2023)

Unsupervised Machine Learning to Identify Depressive Subtypes
por: Kung, Benson, et al.
Publicado: (2022)

Unsupervised Machine Learning to Identify Separable Clinical Alzheimer’s Disease Sub-Populations
por: Prakash, Jayant, et al.
Publicado: (2021)

Unsupervised machine learning identifies predictive progression markers of IPF
por: Pan, Jeanny, et al.
Publicado: (2022)

Machine Learning LHC likelihoods
por: Reyes-González, Humberto
Publicado: (2022)

Population-Based Approaches to Dementia Prevention
por: Sexton, Claire E., et al.
Publicado: (2019)

Reduction in Fall Rate in Dementia Managed Care Through Video Incident Review: Pilot Study
por: Bayen, Eleonore, et al.
Publicado: (2017)

Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data
por: Eshaghi, Arman, et al.
Publicado: (2021)

Machine Learning for Likelihood Free Inference
por: Le Pottier, Luc Tomas
Publicado: (2019)

Similarities and differences in Alzheimer’s dementia comorbidities in racialized populations identified from electronic medical records
por: Woldemariam, Sarah R., et al.
Publicado: (2023)

Identifying population segments for effective intervention design and targeting using unsupervised machine learning: an end-to-end guide
por: Engl, Elisabeth, et al.
Publicado: (2019)

MSR37 Identifying Clinical Subtypes of Long COVID: An Unsupervised Machine Learning Approach
por: Munsell, M., et al.
Publicado: (2023)

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool
por: Celecia, Alimed, et al.
Publicado: (2021)

Unsupervised Machine Learning Identified Distinct Population Clusters Based on Symptoms of Oral Pain, Psychological Distress, and Sleep Problems
por: Chuinsiri, Nontawat
Publicado: (2021)

Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer
por: Gal, Jocelyn, et al.
Publicado: (2020)

Unsupervised Machine Learning Identifies Quantifiable Patterns of Visual Field Loss in Idiopathic Intracranial Hypertension
por: Doshi, Hiten, et al.
Publicado: (2021)

Author Correction: Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data
por: Eshaghi, Arman, et al.
Publicado: (2021)

Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review
por: Sun, Jin, et al.
Publicado: (2022)

Semi-supervised and unsupervised machine learning: novel strategies
por: Albalate, Amparo, et al.
Publicado: (2011)

Keratoconus severity identification using unsupervised machine learning
por: Yousefi, Siamak, et al.
Publicado: (2018)

Recreation of the periodic table with an unsupervised machine learning algorithm
por: Kusaba, Minoru, et al.
Publicado: (2021)

Observing flow of He II with unsupervised machine learning
por: Wen, X., et al.
Publicado: (2022)

Unsupervised machine learning discovers classes in aluminium alloys
por: Bhat, Ninad, et al.
Publicado: (2023)

Handling DNA malfunctions by unsupervised machine learning model
por: Khazaaleh, Mutaz Kh., et al.
Publicado: (2023)

Machine Learning to Predict the Likelihood of Acute Myocardial Infarction
por: Than, Martin P., et al.
Publicado: (2019)

Awareness of Memory Deficits in Early Stage Huntington's Disease
por: Cleret de Langavant, Laurent, et al.
Publicado: (2013)

The specific role of the striatum in interval timing: The Huntington’s disease model
por: Lemoine, Laurie, et al.
Publicado: (2021)

Glaucomatous Patterns in Frequency Doubling Technology (FDT) Perimetry Data Identified by Unsupervised Machine Learning Classifiers
por: Bowd, Christopher, et al.
Publicado: (2014)

Clinical feature-related single-base substitution sequence signatures identified with an unsupervised machine learning approach
por: Ji, Hongchen, et al.
Publicado: (2021)

A Visualization Tool for Cryo-EM Protein Validation with an Unsupervised Machine Learning Model in Chimera Platform
por: Chen, Lin, et al.
Publicado: (2019)

Unsupervised machine learning for detection of faulty beam position monitors
por: Fol, Elena, et al.
Publicado: (2019)

Unsupervised process monitoring and fault diagnosis with machine learning methods
por: Aldrich, Chris, et al.
Publicado: (2013)

Digging deep into Golgi phenotypic diversity with unsupervised machine learning
por: Hussain, Shaista, et al.
Publicado: (2017)

Detection of overdose and underdose prescriptions—An unsupervised machine learning approach
por: Nagata, Kenichiro, et al.
Publicado: (2021)

Clusters of people with type 2 diabetes in the general population: unsupervised machine learning approach using national surveys in Latin America and the Caribbean
por: Carrillo-Larco, Rodrigo M, et al.
Publicado: (2021)

Unsupervised learning of Swiss population spatial distribution
por: Kanevski, Mikhail
Publicado: (2021)

Behavioral and Neural Correlates of Communication via Pointing
por: Cleret de Langavant, Laurent, et al.
Publicado: (2011)

Identifying and evaluating clinical subtypes of Alzheimer’s disease in care electronic health records using unsupervised machine learning
por: Alexander, Nonie, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_e4rip2ljd7s59ul1e0d5tmaj87