在临床决策系统中涉及基于规则的推理和机器学习的混合架构的分类:范围审查
Slawomir Kierner1, Jacek Kucharski2, Zofia Kierner3
1Lodz University of Technology, Faculty of Electrical, Electronic, Computer and Control Engineering, 27 Isabella Street, 02116 Boston, MA, USA.
混合临床决策系统将人工智能 (AI) 和机器学习 (ML) 与医疗规则相结合. 平行架构为临床决策支持提供了更大的透明度和可解释性.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
背景情况:
- 在医疗保健中增加AI和机器学习 (ML) 需要将医学知识 (临床规则) 与ML的预测能力相结合.
- 在临床决策中,平衡ML的高精度与基于规则的系统的透明度是临床决策的关键挑战.
研究的目的:
- 审查混合架构的文献,结合ML和临床规则.
- 识别和评估这些混合系统中的模式,以开发透明和精确的临床决策系统.
主要方法:
- 在PubMed,IEEE Explore和Google Scholar (1992-2022) 中使用"临床决策系统"和"混合临床架构"等关键词进行系统的文献搜索.
- 选了957篇论文,审查了71篇,并使用拟议的分类法对混合架构进行了分类.
- 两个领域专家审查论文以尽量减少偏见,第三个确保一致性.
主要成果:
- 确定了五种混合架构原型:嵌入ML中的规则 (REML),ML规则预处理 (MLRB),ML规则预处理 (RBML),影响ML训练的规则 (RMLT) 和并行规则和ML集合 (PERML).
- REML是最常见的架构,通常优先考虑准确性而不是透明度.
- 很少观察到PERML架构,但有可能提高可解释性.
结论:
- 当前的混合系统往往有利于预测准确性,导致复杂的,不那么透明的设计.
- 平行 (PERML) 架构为更透明和可解释的临床决策支持系统提供了一个有希望的途径.
- 建议对并行架构进行进一步的研究,以提高对人工智能驱动的医疗保健的信任和理解.
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