关于机器学习预测器可信度评估的共识声明
Alessandra Aldieri1, Thiranja Prasad Babarenda Gamage2, Antonino Amedeo La Mattina3,4
1Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24 - 10129 Torino, Italy.
Briefings in bioinformatics
|March 10, 2025
概括
本文建立了12个机器学习 (ML) 预测器的可信度标准. 它通过专注于因果知识和偏见减少,确保了对医疗保健决策的可靠ML工具.
科学领域:
- 在形医学中.
- 生物医学研究的研究.
- 医疗保健中的人工智能
背景情况:
- 机器学习 (ML) 预测器在in silico医学中越来越多地用于估计复杂的生物量.
- 确保这些ML预测因素的可信性对于高风险的临床和生物医学决策至关重要.
- 现有的评估框架可能无法完全解决生物医学应用中ML所带来的独特挑战.
研究的目的:
- 提出关于评估ML预测因子可信度的理论基础的共识声明.
- 概述指导严格评估和部署医疗保健中的ML工具的12个关键陈述.
- 建议策略,以确保ML预测器的可靠性和适用性,考虑因果知识和偏见.
主要方法:
- 由In Silico世界实践社区的专家组成的共识声明的制定.
- 为ML预测器可信度评估制定了12个基本陈述.
- 对ML预测因素和生物物理模型进行比较分析,以确定独特的挑战.
主要成果:
- 一个ML预测器可信度的理论框架,强调因果知识,错误量化和偏差强度.
- 识别与隐性因果知识相关的ML预测器中的特定挑战.
- 拟议的战略,以提高ML预测器在生物医学环境中的可靠性和适用性.
结论:
- 严格评估ML预测因子的可信性对于安全有效地使用in silico医学至关重要.
- 整合因果推理和解决偏见对于医疗保健中可靠的ML应用至关重要.
- 拟议的框架旨在指导研究人员,开发人员和监管机构对机器学习工具的负责任评估和部署.
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