Sun Kai, Roy Arkajyoti, Tobin Joshua M
Department 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.
Department of Management Science and Statistics, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA.
J Crit Care. 2024 Aug;82:154792. doi: 10.1016/j.jcrc.2024.154792. Epub 2024 Mar 29.
With increasing computing power, artificial intelligence (AI) and machine learning (ML) have prospered, which facilitate the analysis of large datasets, especially those found in critical care. It is important to define these terminologies, to inform a standardized approach to critical care research. This manuscript hopes to clarify these terms with examples from medical literature. Three major components that are required for a successful ML implementation: (i) reliable dataset, (ii) ML algorithm, and (iii) unbiased model evaluation, are discussed. A reliable dataset can be structured or unstructured with limited noise, outliers, and missing values. ML, a subset of AI, is typically focused on supervised or unsupervised learning tasks in which the output is based on inputs and derived from iterative pattern recognition algorithms, while AI is the overall ability of a machine to "think" or mimic human behavior; and to analyze data free from human influence. Even with successful implementation, advanced AI and ML algorithms have faced challenges in adoption into practice, mainly due to their lack of interpretability, which hinders trust, buy-in, and engagement from clinicians. Consequently, traditional algorithms, such as linear and logistic regression, that may have reduced predictive power but are highly interpretable, continue to be widely used.
随着计算能力的不断提高，人工智能（AI）和机器学习（ML）蓬勃发展，这有助于对大型数据集进行分析，尤其是在重症监护中发现的数据集。定义这些术语很重要，以便为重症监护研究提供一种标准化方法。本手稿希望通过医学文献中的例子来澄清这些术语。讨论了成功实施机器学习所需的三个主要组成部分：（i）可靠的数据集，（ii）机器学习算法，以及（iii）无偏模型评估。可靠的数据集可以是结构化的或非结构化的，具有有限的噪声、异常值和缺失值。机器学习是人工智能的一个子集，通常专注于监督或无监督学习任务，其中输出基于输入并源自迭代模式识别算法，而人工智能是机器“思考”或模仿人类行为的总体能力；以及分析不受人类影响的数据。即使成功实施，先进的人工智能和机器学习算法在实际应用中也面临挑战，主要是因为它们缺乏可解释性，这阻碍了临床医生的信任、接受和参与。因此，传统算法，如线性回归和逻辑回归，虽然预测能力可能较低，但具有高度可解释性，仍然被广泛使用。
