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在使用真实世界数据时,对公平的机器学习技术进行范围审查.

Yu Huang1, Jingchuan Guo2, Wei-Han Chen2

  • 1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.

Journal of biomedical informatics
|March 7, 2024
PubMed
概括

医疗保健AI/ML模型中的算法偏差可能会加剧健康不平等. 本次审查强调了关于减轻现实数据偏差的有限研究,并呼吁更多地关注人工智能应用中的公平性.

关键词:
减轻偏见的偏见公平的机器学习现实世界的数据数据.

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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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