尽量减少和量化人工智能决策中的不确定性:在医学中的应用
Samuel D Curtis1,2,3,4,5, Sambit Panda6,7, Adam Li8
1Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205.
概括
多维信息化通用假设测试 (MIGHT) 准确量化AI预测的不确定性,并控制特定的错误类型. 这种人工智能策略可用于真实世界的数据分析,
科学领域:
- 人工智能
- 生物医学数据分析
- 统计学学习
背景情况:
- 人工智能对于数据分析至关重要,但控制特定的错误类型 (例如选中的错误阳性) 是具有挑战性的.
- 在AI预测中量化不确定性,特别是在错误控制方面,存在理论和实践上的困难.
研究的目的:
- 开发一种用于准确量化AI预测不确定性的新策略.
- 在人工智能驱动的分析中确保对特定错误类型的可靠控制.
- 在生物医学查等关键应用中信任基于人工智能的发现的挑战.
主要方法:
- 介绍了多维知情通用假设测试 (MIGHT),一种非参数组合方法.
- 在MIGHT框架内进行综合规范交叉验证和参数校准.
- 通过模拟和应用液体活检数据 (ccfDNA) 验证了MIGHT的性能.
主要成果:
- 通过理论上的保证,MIGHT 准确地量化了不确定性和信心,超过了典型的人工智能方法.
- 与SVM,随机森林和变形机相比,MIGHT在ccfDNA数据上表现出明显较低的变化系数和更高的灵敏度.
- 发现结合变量组可以降低由于噪声增加的灵敏度,强调最佳变量选择的重要性.
结论:
- MIGHT为基于人工智能的数据分析提供了一种可靠的方法,特别是在控制特定错误类型至关重要的情况下.
- 这项研究强调了用理论上的保证来量化不确定性和信心,以便对现实数据进行可靠的解释.
- MIGHT为液体活检中的生物标志物发现和验证提供了强大的解决方案,解决了癌症检测中的一个悬而未决的问题.
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