人工智能和机器学习:在重症监护研究中的术语和当前概念的定义
Kai Sun1, Arkajyoti Roy2, Joshua M Tobin3
1Department of Management Science and Statistics, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA; Department of Anesthesiology, University of Texas Health Sciences Center San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229, USA.
Journal of critical care
|March 30, 2024
概括
人工智能 (AI) 和机器学习 (ML) 是用于重症监护数据分析的强大工具. 定义AI和ML对于标准化研究至关重要,解决临床采用解释性等挑战.
科学领域:
- 关键护理医学 关键护理医学
- 医疗信息学医学信息学
- 计算生物学是一种计算生物学.
背景情况:
- 计算能力的进步推动了人工智能 (AI) 和机器学习 (ML) 的发展.
- 这些技术为分析在重症监护机构中普遍存在的大数据集提供了巨大的潜力.
- 需要标准化定义来指导重症监护的研究和实施.
研究的目的:
- 在重症监护研究的背景下,澄清AI和ML的定义.
- 提供医学文献中的例子来说明这些概念.
- 讨论成功实施ML的关键组成部分.
主要方法:
- 审查和综合现有的AI和ML在重症监护的文献.
- 解释包括监督和无监督学习在内的核心概念.
- 讨论ML的基本要素:可靠的数据集,算法和公正的评估.
主要成果:
- 人工智能指的是机器模仿人类认知功能的能力,而ML则是专注于从数据中识别模式的子集.
- 成功实施ML需要可靠的数据集,适当的算法和公正的模型评估.
- 在临床实践中采用AI/ML的一个重要障碍是缺乏可解释性,这阻碍了临床医生的信任.
结论:
- 对AI和ML的明确定义对于推进重症监护研究至关重要.
- 解释性仍然是临床实践中广泛采用先进AI和ML的关键挑战.
- 传统的,可解释的算法仍然被广泛使用,因为它们的透明度和临床医生容易理解.
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