Developing a fair and interpretable representation of the clock drawing test for mitigating low education and racial bias

The clock drawing test (CDT) is a neuropsychological assessment tool to screen an individual’s cognitive ability. In this study, we developed a Fair and Interpretable Representation of Clock drawing test (FaIRClocks) to evaluate and mitigate classification bias against people with less than 8 years...

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Vydané v:	Scientific reports Ročník 14; číslo 1; s. 17444 - 13
Hlavní autori:	Zhang, Jiaqing, Bandyopadhyay, Sabyasachi, Kimmet, Faith, Wittmayer, Jack, Khezeli, Kia, Libon, David J., Price, Catherine C., Rashidi, Parisa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 29.07.2024 Nature Publishing Group Nature Portfolio
Predmet:	631/378/1595/1636 631/378/1595/1637 631/378/2649 631/378/2649/1310 631/553/117 692/617/375 692/699/375/132/1283 Aged Aged, 80 and over AI Fairness Attention Cognition - physiology Cognitive ability Cognitive Dysfunction - diagnosis Deep Learning Education Educational Status Female Humanities and Social Sciences Humans Male Memory Mental Status and Dementia Tests Middle Aged Mini-mental state examination multidisciplinary Neuropsychological Tests Neuropsychology Racism Relevance factor variational autoencoder Science Science (multidisciplinary) Semi-supervised deep learning Standard scores Mini-mental state examination Semi-supervised deep learning Relevance factor variational autoencoder AI Fairness Memory Attention
ISSN:	2045-2322, 2045-2322
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Popis
Shrnutí:	The clock drawing test (CDT) is a neuropsychological assessment tool to screen an individual’s cognitive ability. In this study, we developed a Fair and Interpretable Representation of Clock drawing test (FaIRClocks) to evaluate and mitigate classification bias against people with less than 8 years of education, while screening their cognitive function using an array of neuropsychological measures. In this study, we represented clock drawings by a priorly published 10-dimensional deep learning feature set trained on publicly available data from the National Health and Aging Trends Study (NHATS). These embeddings were further fine-tuned with clocks from a preoperative cognitive screening program at the University of Florida to predict three cognitive scores: the Mini-Mental State Examination (MMSE) total score, an attention composite z-score (ATT-C), and a memory composite z-score (MEM-C). ATT-C and MEM-C scores were developed by averaging z-scores based on normative references. The cognitive screening classifiers were initially tested to see their relative performance in patients with low years of education (< = 8 years) versus patients with higher education (> 8 years) and race. Results indicated that the initial unweighted classifiers confounded lower education with cognitive compromise resulting in a 100% type I error rate for this group. Thereby, the samples were re-weighted using multiple fairness metrics to achieve sensitivity/specificity and positive/negative predictive value (PPV/NPV) balance across groups. In summary, we report the FaIRClocks model, with promise to help identify and mitigate bias against people with less than 8 years of education during preoperative cognitive screening.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-024-68481-w