Machine learning
Machine learning models trained on physical and behavioral data alone achieved a high classification accuracy (AUC up to 94%), comparable to that of MRI- based models, in differentiating early- and late-stage MCI. Combining physical and behavioral and MRI features yielded marginal improvements in the prediction performance.
Accurate identification of individuals at high risk for mild cognitive impairment (MCI) among chronic heart failure (CHF) patients is crucial for reducing rehospitalization and mortality rates. This study aimed to develop and validate a machine learning model to predict MCI risk in CHF patients. 602 CHF patients were included in this cross-sectional analysis. We constructed four machine ...
Key Details About Machine Learning Based Prediction Of Mild Cognitive Impairment
With the above evidence, previous studies mainly focused on machine learning to classify MCI or dementia from normal controls with cross-section image variables, methods on combination of clinical and image variables to find the risk through a dynamic progression from normal cognitive function to MCI have not been well explored.
Mild cognitive impairment (MCI) refers to a stage of cognitive decline that precedes dementia and is characterized by noticeable cognitive impairment while maintaining the ability to perform daily ...
Machine learning based digital assessment of mild cognitive impairment ...
These results indicate that fine-grained mouse-movement features from the cTMT provide useful information for classifying mild cognitive impairment and for predicting multiple neuropsychological ...
It is estimated that the number of people with dementia will reach 78 million by 2030 and 139 million by 2050, costing over 2.8 trillion dollars worldwide.1 Effective screening for mild cognitive impairment (MCI) as a risk factor for developing Alzheimer's disease (AD) is a crucial step in helping aging population with their needs.2 Early detection and automated screening for MCI and dementia ...
Machine learning prediction of mild cognitive impairment and its ...
Machine learning prediction of mild cognitive impairment and its progression to Alzheimer's disease Sajjad Fouladvand Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA Correspondence Sajjad Fouladvand, Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Abstract Background Early identification of individuals who progress from normal cognition (NC) to mild cognitive impairment (MCI) may help prevent cognitive decline. We aimed to build predictive models using radiomic features of the bilateral hippocampus in combination with scores from neuropsychological assessments.
Machine learning
Background Cognitive impairment is a prominent non-motor manifestation of Parkinson's disease (PD) and is associated with reduced quality of life, increased mortality, and higher healthcare utilization. We aimed to develop and externally validate a machine-learning model, trained on Montreal Cognitive Assessment (MoCA)—based Movement Disorder Society (MDS) Level I labels, that estimates ...
