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FaIRClocks: Fair and Interpretable Representation of the Clock Drawing Test for mitigating classifier bias against lower educational groups

The clock drawing test (CDT) is a neuropsychological assessment tool to evaluate a patient’s cognitive ability. In this study, we developed a Fair and Interpretable Representation of Clock drawing tests (FaIRClocks) to evaluate and mitigate bias against people with lower education while predicting t...

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Autores principales: Zhang, Jiaqing, Bandyopadhyay, Sabyasachi, Kimmet, Faith, Wittmayer, Jack, Khezeli, Kia, Libon, David J., Price, Catherine C., Rashidi, Parisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602062/
https://www.ncbi.nlm.nih.gov/pubmed/37886534
http://dx.doi.org/10.21203/rs.3.rs-3398970/v1
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author Zhang, Jiaqing
Bandyopadhyay, Sabyasachi
Kimmet, Faith
Wittmayer, Jack
Khezeli, Kia
Libon, David J.
Price, Catherine C.
Rashidi, Parisa
author_facet Zhang, Jiaqing
Bandyopadhyay, Sabyasachi
Kimmet, Faith
Wittmayer, Jack
Khezeli, Kia
Libon, David J.
Price, Catherine C.
Rashidi, Parisa
author_sort Zhang, Jiaqing
collection PubMed
description The clock drawing test (CDT) is a neuropsychological assessment tool to evaluate a patient’s cognitive ability. In this study, we developed a Fair and Interpretable Representation of Clock drawing tests (FaIRClocks) to evaluate and mitigate bias against people with lower education while predicting their cognitive status. We represented clock drawings with a 10-dimensional latent embedding using Relevance Factor Variational Autoencoder (RF-VAE) network pretrained on publicly available clock drawings from the National Health and Aging Trends Study (NHATS) dataset. These embeddings were later fine-tuned for predicting three cognitive scores: the Mini-Mental State Examination (MMSE) total score, attention composite z-score (ATT-C), and memory composite z-score (MEM-C). The classifiers were initially tested to see their relative performance in patients with low education (<= 8 years) versus patients with higher education (> 8 years). Results indicated that the initial unweighted classifiers confounded lower education with cognitive impairment, resulting in a 100% type I error rate for this group. Thereby, the samples were re-weighted using multiple fairness metrics to achieve balanced performance. In summary, we report the FaIRClocks model, which a) can identify attention and memory deficits using clock drawings and b) exhibits identical performance between people with higher and lower education levels.
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spelling pubmed-106020622023-10-27 FaIRClocks: Fair and Interpretable Representation of the Clock Drawing Test for mitigating classifier bias against lower educational groups Zhang, Jiaqing Bandyopadhyay, Sabyasachi Kimmet, Faith Wittmayer, Jack Khezeli, Kia Libon, David J. Price, Catherine C. Rashidi, Parisa Res Sq Article The clock drawing test (CDT) is a neuropsychological assessment tool to evaluate a patient’s cognitive ability. In this study, we developed a Fair and Interpretable Representation of Clock drawing tests (FaIRClocks) to evaluate and mitigate bias against people with lower education while predicting their cognitive status. We represented clock drawings with a 10-dimensional latent embedding using Relevance Factor Variational Autoencoder (RF-VAE) network pretrained on publicly available clock drawings from the National Health and Aging Trends Study (NHATS) dataset. These embeddings were later fine-tuned for predicting three cognitive scores: the Mini-Mental State Examination (MMSE) total score, attention composite z-score (ATT-C), and memory composite z-score (MEM-C). The classifiers were initially tested to see their relative performance in patients with low education (<= 8 years) versus patients with higher education (> 8 years). Results indicated that the initial unweighted classifiers confounded lower education with cognitive impairment, resulting in a 100% type I error rate for this group. Thereby, the samples were re-weighted using multiple fairness metrics to achieve balanced performance. In summary, we report the FaIRClocks model, which a) can identify attention and memory deficits using clock drawings and b) exhibits identical performance between people with higher and lower education levels. American Journal Experts 2023-10-09 /pmc/articles/PMC10602062/ /pubmed/37886534 http://dx.doi.org/10.21203/rs.3.rs-3398970/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Zhang, Jiaqing
Bandyopadhyay, Sabyasachi
Kimmet, Faith
Wittmayer, Jack
Khezeli, Kia
Libon, David J.
Price, Catherine C.
Rashidi, Parisa
FaIRClocks: Fair and Interpretable Representation of the Clock Drawing Test for mitigating classifier bias against lower educational groups
title FaIRClocks: Fair and Interpretable Representation of the Clock Drawing Test for mitigating classifier bias against lower educational groups
title_full FaIRClocks: Fair and Interpretable Representation of the Clock Drawing Test for mitigating classifier bias against lower educational groups
title_fullStr FaIRClocks: Fair and Interpretable Representation of the Clock Drawing Test for mitigating classifier bias against lower educational groups
title_full_unstemmed FaIRClocks: Fair and Interpretable Representation of the Clock Drawing Test for mitigating classifier bias against lower educational groups
title_short FaIRClocks: Fair and Interpretable Representation of the Clock Drawing Test for mitigating classifier bias against lower educational groups
title_sort fairclocks: fair and interpretable representation of the clock drawing test for mitigating classifier bias against lower educational groups
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602062/
https://www.ncbi.nlm.nih.gov/pubmed/37886534
http://dx.doi.org/10.21203/rs.3.rs-3398970/v1
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