AIMC Topic: Dementia

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Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods.

Journal of Alzheimer's disease : JAD
BACKGROUND: Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it...

Social Robot Interventions for People with Dementia: A Systematic Review on Effects and Quality of Reporting.

Journal of Alzheimer's disease : JAD
BACKGROUND: Using non-pharmacological interventions is a current approach in dementia care to manage responsive behaviors, to maintain functional capacity, and to reduce emotional stress. Novel technologies such as social robot interventions might be...

PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Developing algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to s...

Deep learning with long short-term memory networks for classification of dementia related travel patterns.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Wandering pattern classification is important for early recognition of cognitive deterioration and other health conditions in people with dementia (PWD). In this paper, we leverage the orientation data available on mobile devices to recognize dementi...

Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach.

International psychogeriatrics
OBJECTIVES: To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed dementia and those who did not.

Predictors of Dementia in the Oldest Old: A Novel Machine Learning Approach.

Alzheimer disease and associated disorders
BACKGROUND: Incidence of dementia increases exponentially with age; little is known about its risk factors in the ninth and 10th decades of life. We identified predictors of dementia with onset after age 85 years in a longitudinal population-based co...

Predicting Cognitive Impairment and Dementia: A Machine Learning Approach.

Journal of Alzheimer's disease : JAD
BACKGROUND: Efforts to identify important risk factors for cognitive impairment and dementia have to date mostly relied on meta-analytic strategies. A comprehensive empirical evaluation of these risk factors within a single study is currently lacking...