Article

Machine learning identifies novel markers predicting functional decline in older adults

2020-08-25

Abstract excerpt

<p>The ability to carry out instrumental activities of daily living, such as paying bills, remembering appointments, and shopping alone decreases with age, yet there are remarkable individual differences in the rate of decline among older adults. Understanding variables associated with decline in instrumental activities of daily living is critical to providing appropriate intervention to prolong independence. Prio...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 940b85cf-2084-5a77-878a-1039c00eaebe
DOI: 10.31234/osf.io/xg3ab

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine learning identifies novel markers predicting functional decline in older adultsDOI 10.31234/osf.io/xg3abRead

Machine Learning methods for predicting conversion from mild cognitive impairment to Alzheimer’s disease. A systematic review.Semantic proximity Read

Predicting Continuous Cognitive Decline: The Generalizability of a Multimodal Machine Learning Approach Including Structural MRI and Non-Brain DataSemantic proximity Read

Screening for early-stage Alzheimer’s disease using optimized feature sets and machine learningSemantic proximity Read

Comprehensive evaluation of AT(N) imaging biomarkers for predicting cognitionSemantic proximity Read

Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and AustraliaSemantic proximity Read

Aging as an active player in Alzheimer’s Disease Classification: Insights from feature selection in BrainAge ModelsSemantic proximity Read

Machine learning models for dementia risk prediction: Evidence from the Sydney Memory and Ageing StudySemantic proximity Read

Machine learning-based personalized composite score dissects risk and protective factors for cognitive and motor function in elderlySemantic proximity Read

Machine Learning Predicts Conversion from Normal Aging to Mild Cognitive Impairment Using Medical History, APOE Genotype, and Neuropsychological Assessment.Semantic proximity Read

A Machine Learning Approach to Predict Cognitive Decline in Alzheimer’s Disease Clinical TrialsSemantic proximity Read

Machine learning-based personalized composite score dissects risk and protective factors for cognitive and motor function in elderlySemantic proximity Read

A Novel Ensemble-Based Machine Learning Algorithm To Predict The Conversion From Mild Cognitive Impairment To Alzheimer’s Disease Using Socio-demographic Characteristics, Clinical Information And Neuropsychological MeasuresSemantic proximity Read