AIMC Topic: Neuropsychological Tests
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Are the criteria for PD-MCI diagnosis comprehensive? A Machine Learning study with modified criteria.
Parkinsonism & related disorders
Apr 30, 2024
BACKGROUND: Mild cognitive impairment in Parkinson's disease (PD-MCI) includes deficits in different cognitive domains, and one domain to explore for neurocognitive impairment following the DSM-V is social cognition. However, this domain is not inclu...
Discourse- and lesion-based aphasia quotient estimation using machine learning.
NeuroImage. Clinical
Apr 6, 2024
Discourse is a fundamentally important aspect of communication, and discourse production provides a wealth of information about linguistic ability. Aphasia commonly affects, in multiple ways, the ability to produce discourse. Comprehensive aphasia as...
Machine-based learning of multidimensional data in bipolar disorder - pilot results.
Bipolar disorders
Mar 26, 2024
INTRODUCTION: Owing to the heterogenic picture of bipolar disorder, it takes approximately 8.8 years to reach a correct diagnosis. Early recognition and early intervention might not only increase quality of life, but also increase life expectancy as ...
Predictive deep learning models for cognitive risk using accessible data.
Bioscience trends
Feb 20, 2024
The early detection of mild cognitive impairment (MCI) is crucial to preventing the progression of dementia. However, it necessitates that patients voluntarily undergo cognitive function tests, which may be too late if symptoms are only recognized on...
Robotic assessment of sensorimotor and cognitive deficits in patients with temporal lobe epilepsy.
Epilepsy & behavior : E&B
Jan 5, 2024
OBJECTIVE: Individuals with temporal lobe epilepsy (TLE) frequently demonstrate impairments in executive function, working memory, and/or declarative memory. It is recommended that screening for cognitive impairment is undertaken in all people newly ...
Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands.
Scientific reports
May 6, 2023
The clock drawing test is a simple and inexpensive method to screen for cognitive frailties, including dementia. In this study, we used the relevance factor variational autoencoder (RF-VAE), a deep generative neural network, to represent digitized cl...
A machine-learning algorithm for predicting brain age using Rey-Osterrieth complex figure tests of healthy participants.
Applied neuropsychology. Adult
Jan 12, 2023
OBJECTIVE: Neuropsychologists widely use the Rey-Osterrieth complex figure test (RCFT) as part of neuropsychological test batteries to evaluate cognitive function and assess constructional ability, with age being the most significant factor. Our stud...
Application of machine learning techniques for dementia severity prediction from psychometric tests in the elderly population.
Applied neuropsychology. Adult
Jan 1, 2023
Previous research has shown the benefits of early detection and treatment of dementia. This detection is usually performed manually by one or more clinicians based on reports and psychometric testing. Machine learning algorithms provide an alternativ...
Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records.
BMC medical informatics and decision making
Oct 17, 2022
BACKGROUND: Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mainstream diagnostic tests and screening tools, such as CAMCOG and MMSE, often fail to detect dementia...
Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jul 7, 2022
INTRODUCTION: Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia.
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