论文标题
处理医疗保健数据中缺失价值：对基于深度学习的插补技术的系统评价
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
论文作者
论文摘要
目的：正确处理缺失值对于提供可靠的估计和决策至关重要，尤其是在诸如临床研究之类的高风险领域。数据的不断增长和复杂性导致许多研究人员开发了深度学习（DL）基于基于的插补技术。我们进行了系统的审查，以评估这些技术的使用，特别关注数据类型，旨在帮助来自各个学科的医疗保健研究人员处理缺失的价值。 方法：我们搜索了五个数据库（Medline，Web of Science，Embase，Cinahl和Scopus），以在2021年8月之前发表的文章，这些文章将基于DL的模型应用于插图。我们从四个角度评估了选定的出版物：健康数据类型，模型骨干（即主要体系结构），归纳策略以及与非基于非DL的方法的比较。根据数据类型，我们创建了一个证据图，以说明采用DL模型。 结果：我们包括了64篇文章，其中最频繁地研究了表格静态（26.6％，17/64）和时间数据（37.5％，24/64）。我们发现，模型骨干在数据类型和插补策略之间有所不同。 “综合”策略，即与下游任务同时解决的归纳任务，在表格时间（50％，12/24）和多模式数据（71.4％，5/7）中很受欢迎，但对于其他数据类型而言有限。此外，与非基于非DL的方法相比，在大多数研究中，基于DL的插补方法在大多数研究中产生了更好的归合精度。 结论：基于DL的插补模型可以根据数据类型进行自定义，解决相应的丢失模式及其相关的“集成”策略可以增强插补的功效，尤其是在数据复杂的情况下。未来的研究可能关注基于DL的医疗保健数据插补模型的可移植性和公平性。
Objective: The proper handling of missing values is critical to delivering reliable estimates and decisions, especially in high-stakes fields such as clinical research. The increasing diversity and complexity of data have led many researchers to develop deep learning (DL)-based imputation techniques. We conducted a systematic review to evaluate the use of these techniques, with a particular focus on data types, aiming to assist healthcare researchers from various disciplines in dealing with missing values. Methods: We searched five databases (MEDLINE, Web of Science, Embase, CINAHL, and Scopus) for articles published prior to August 2021 that applied DL-based models to imputation. We assessed selected publications from four perspectives: health data types, model backbone (i.e., main architecture), imputation strategies, and comparison with non-DL-based methods. Based on data types, we created an evidence map to illustrate the adoption of DL models. Results: We included 64 articles, of which tabular static (26.6%, 17/64) and temporal data (37.5%, 24/64) were the most frequently investigated. We found that model backbone(s) differed among data types as well as the imputation strategy. The "integrated" strategy, that is, the imputation task being solved concurrently with downstream tasks, was popular for tabular temporal (50%, 12/24) and multi-modal data (71.4%, 5/7), but limited for other data types. Moreover, DL-based imputation methods yielded better imputation accuracy in most studies, compared with non-DL-based methods. Conclusion: DL-based imputation models can be customized based on data type, addressing the corresponding missing patterns, and its associated "integrated" strategy can enhance the efficacy of imputation, especially in scenarios where data is complex. Future research may focus on the portability and fairness of DL-based models for healthcare data imputation.
