论文标题
数据协调数字医疗保健中的信息融合：最先进的系统评价，荟萃分析和未来的研究方向
Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions
论文作者
论文摘要
在大规模数字医疗研究中，消除多中心数据的偏见和差异一直是一个挑战，该研究需要能够整合从不同扫描仪和协议获得的数据中提取的临床特征，以提高稳定性和稳健性。先前的研究描述了融合单个模态多中心数据集的各种计算方法。但是，这些调查很少关注评估指标，并且缺乏计算数据协调研究的清单。在这项系统的综述中，我们总结了数字医疗领域中多模式数据的计算数据协调方法，包括基于不同理论的协调策略和评估指标。此外，提出了一份综合的清单，总结了数据协调研究的常见实践，以指导研究人员更有效地报告其研究结果。最后但并非最不重要的一点是，提出了提供方法论和指标选择的可能方法的流程图，并调查了不同方法的局限性以进行未来的研究。
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.
