PDF(Pubmed)
Abstract：
Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019-2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles\' search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019-2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence.
摘要：
机器学习（ML）是一个广泛的术语，涵盖了几种允许我们从数据中学习的方法。这些方法可以允许将大型现实世界数据库更快速地转换为应用程序以通知患者提供者决策。本文介绍了在2019-2023年间使用傅里叶变换红外（FTIR）光谱和ML进行人体血液分析的文章。进行了文献综述，以确定已发表的与FTIR相关的ML研究，以区分病理性和健康人血细胞。实施了文章搜索策略，并对符合资格标准的研究进行了评估。与研究设计相关的相关数据，统计方法，并确定了优势和局限性。在过去5年（2019-2023年）中，共确定并评估了39篇出版物。不同的方法，统计软件包，和方法在确定的研究中使用。最常见的方法包括支持向量机（SVM）和主成分分析（PCA）方法。大多数研究应用了内部验证，并采用了不止一种算法，而只有四项研究对数据应用了一种ML算法。各种各样的方法，算法，统计软件,并在ML方法的应用中采用了验证策略。需要确保使用多种ML方法，模型选择策略明确，内部和外部验证都是必要的，以确保人类血细胞的辨别是以最高有效的证据进行的。
