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

Autor:	Zhang J; Department of Electrical and Computer Engineering, University of Florida.; Perioperative Cognitive Anesthesia Network, University of Florida., Bandyopadhyay S; J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida.; Perioperative Cognitive Anesthesia Network, University of Florida., Kimmet F; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida.; Department of Anesthesiology, College of Medicine, University of Florida., Wittmayer J; Intelligent Critical Care Center (IC3), University of Florida., Khezeli K; J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida.; Intelligent Critical Care Center (IC3), University of Florida., Libon DJ; Department of Anesthesiology, College of Medicine, University of Florida., Price CC; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida.; Department of Anesthesiology, College of Medicine, University of Florida.; Perioperative Cognitive Anesthesia Network, University of Florida., Rashidi P; J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida.
Jazyk:	angličtina
Zdroj:	Research square [Res Sq] 2023 Oct 09. Date of Electronic Publication: 2023 Oct 09.
DOI:	10.21203/rs.3.rs-3398970/v1
Abstrakt:	The clock drawing test (CDT) is a neuropsychological assessment tool to evaluate a patient's cognitive ability. In this study, we developed a Fa ir and I nterpretable R epresentation 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. Competing Interests: Competing interests The author(s) declare no competing interests.
Databáze:	MEDLINE
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