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Updated: May 2, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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评估和改进用于COVID-19严重程度分类的算法公平性,使用基于可解释的人工智能的偏差缓解

Shayan Nejadshamsi1,2,3, Charlene H Chu4,5, Katherine S McGilton4,5

  • 1Mila-Quebec AI Institute, Montreal, QC H2S 3H1, Canada.

JAMIA open
|January 12, 2026
PubMed
概括

这项研究开发了一种可解释的AI (XAI) 方法,以减少COVID-19严重性预测模型中的性别偏见. XAI方法提高了公平性,但没有显著影响模型的准确性,确保老年人获得公平的医疗保健.

关键词:
COVID-19 的严重程度.偏见缓解和减轻偏见的方法可以解释的人工智能AI公平的公平的公平.机器学习分类器机器学习分类器

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