Inproceedings
1.
Chloe M. Barnes
A Study on Psychometric Assessment Data for Autonomous Dementia Detection Inproceedings
In: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 383–389, Association for Computing Machinery, Corfu, Greece, 2022.
Abstract | Links | BibTeX | Tags: AI and Health, Dementia Classification, Machine Learning, Multi-Modal Data
@inproceedings{10.1145/3529190.3534726,
title = {A Study on Psychometric Assessment Data for Autonomous Dementia Detection},
author = {Chloe M. Barnes},
url = {https://doi.org/10.1145/3529190.3534726},
doi = {10.1145/3529190.3534726},
year = {2022},
date = {2022-06-29},
urldate = {2022-01-01},
booktitle = {Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments},
pages = {383--389},
publisher = {Association for Computing Machinery},
address = {Corfu, Greece},
series = {PETRA '22},
abstract = {Dementia and Alzheimer’s disease are characterised by cognitive decline, and diagnoses are predicted to rise due to an ageing population. Psychometric assessments are widely used by clinicians to inform the diagnosis of dementia, however these may not be as accurate or accessible for patients with lower levels of literacy or socioeconomic status. This study explores how machine learning models can detect dementia when trained on combinations of attributes from multi-modal datasets containing psychometric and Magnetic Resonance Imaging (MRI) data. When psychometric testing is not available, results show that the Random Forest classifier achieves a balanced accuracy, sensitivity and specificity of 84.75%, 79.10%, and 90.41% respectively before the dataset was standardised, and 84.34%, 78.27%, and 90.41% after – outperforming identical models trained on data from a single psychometric test.},
keywords = {AI and Health, Dementia Classification, Machine Learning, Multi-Modal Data},
pubstate = {published},
tppubtype = {inproceedings}
}
Dementia and Alzheimer’s disease are characterised by cognitive decline, and diagnoses are predicted to rise due to an ageing population. Psychometric assessments are widely used by clinicians to inform the diagnosis of dementia, however these may not be as accurate or accessible for patients with lower levels of literacy or socioeconomic status. This study explores how machine learning models can detect dementia when trained on combinations of attributes from multi-modal datasets containing psychometric and Magnetic Resonance Imaging (MRI) data. When psychometric testing is not available, results show that the Random Forest classifier achieves a balanced accuracy, sensitivity and specificity of 84.75%, 79.10%, and 90.41% respectively before the dataset was standardised, and 84.34%, 78.27%, and 90.41% after – outperforming identical models trained on data from a single psychometric test.
