确定处理临床结构化数据集缺失值的最合适估算方法:系统综述。

IF 3.9 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Marziyeh Afkanpour, Elham Hosseinzadeh, Hamed Tabesh
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引用次数: 0

摘要

背景和目标:理解研究数据集对于获得可靠有效的结果至关重要。健康分析师必须深入理解所分析的数据。有了这种理解能力,他们才能提出切实可行的解决方案,处理临床数据源中的缺失数据。准确处理缺失值对于做出精确估计和明智决策至关重要,尤其是在临床研究等关键领域。随着数据的多样性和复杂性不断增加,众多学者开发了一系列估算技术。为此,我们进行了一项系统性综述,根据表格数据集的特征(包括缺失的机制、模式和比例)介绍了各种估算技术,以确定医疗保健领域最合适的估算方法:我们在 PubMed、Web of Science、Scopus 和 IEEE Xplore 四个信息数据库中搜索了截至 2023 年 9 月 20 日发表的文章,这些文章讨论了处理临床结构化数据集中缺失值的估算方法。我们对所选文章的调查主要集中在四个方面:机制、模式、缺失率和各种估算策略。通过综合这些方面的见解,我们构建了一个证据图谱,为处理表格数据集中的缺失值推荐合适的估算方法:在 2955 篇文章中,有 58 篇被纳入分析。根据缺失值的结构以及这些研究中提取的项目所使用的估算方法类型,证据图谱的开发结果显示,45%的研究采用了传统的统计方法,31%的研究采用了机器学习和深度学习方法,24%的研究采用了混合估算技术来处理缺失值:考虑临床数据集中缺失值的结构和特征对于选择最合适的数据估算技术至关重要,尤其是在传统统计方法中。准确估计缺失值以反映实际情况,可提高获得高质量和可重复使用数据的可能性,从而为精确的医疗决策过程做出重大贡献。本综述研究为在数据预处理阶段选择最合适的估算方法提供了指导,以便对结构化临床数据集进行分析处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review.

Background and objectives: Comprehending the research dataset is crucial for obtaining reliable and valid outcomes. Health analysts must have a deep comprehension of the data being analyzed. This comprehension allows them to suggest practical solutions for handling missing data, in a clinical data source. Accurate handling of missing values is critical for producing precise estimates and making informed decisions, especially in crucial areas like clinical research. With data's increasing diversity and complexity, numerous scholars have developed a range of imputation techniques. To address this, we conducted a systematic review to introduce various imputation techniques based on tabular dataset characteristics, including the mechanism, pattern, and ratio of missingness, to identify the most appropriate imputation methods in the healthcare field.

Materials and methods: We searched four information databases namely PubMed, Web of Science, Scopus, and IEEE Xplore, for articles published up to September 20, 2023, that discussed imputation methods for addressing missing values in a clinically structured dataset. Our investigation of selected articles focused on four key aspects: the mechanism, pattern, ratio of missingness, and various imputation strategies. By synthesizing insights from these perspectives, we constructed an evidence map to recommend suitable imputation methods for handling missing values in a tabular dataset.

Results: Out of 2955 articles, 58 were included in the analysis. The findings from the development of the evidence map, based on the structure of the missing values and the types of imputation methods used in the extracted items from these studies, revealed that 45% of the studies employed conventional statistical methods, 31% utilized machine learning and deep learning methods, and 24% applied hybrid imputation techniques for handling missing values.

Conclusion: Considering the structure and characteristics of missing values in a clinical dataset is essential for choosing the most appropriate data imputation technique, especially within conventional statistical methods. Accurately estimating missing values to reflect reality enhances the likelihood of obtaining high-quality and reusable data, contributing significantly to precise medical decision-making processes. Performing this review study creates a guideline for choosing the most appropriate imputation methods in data preprocessing stages to perform analytical processes on structured clinical datasets.

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来源期刊
BMC Medical Research Methodology
BMC Medical Research Methodology 医学-卫生保健
CiteScore
6.50
自引率
2.50%
发文量
298
审稿时长
3-8 weeks
期刊介绍: BMC Medical Research Methodology is an open access journal publishing original peer-reviewed research articles in methodological approaches to healthcare research. Articles on the methodology of epidemiological research, clinical trials and meta-analysis/systematic review are particularly encouraged, as are empirical studies of the associations between choice of methodology and study outcomes. BMC Medical Research Methodology does not aim to publish articles describing scientific methods or techniques: these should be directed to the BMC journal covering the relevant biomedical subject area.
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