Tuly Shelia Rahman, Ranjbari Sima, Murat Ekrem Alper, Arslanturk Suzan
Department of Computer Science, Wayne State University, 5057 Woodward Ave, Detroit, 48201, MI, USA.
Department of Industrial and Systems Engineering, Wayne State University, 4th Street, Detroit, 48201, MI, USA.
Comput Biol Med. 2025 Jun;191:110108. doi: 10.1016/j.compbiomed.2025.110108. Epub 2025 Apr 9.
The integration of data from diverse sources is not only crucial for addressing data scarcity in health informatics but also enables the use of complementary information from multiple datasets. However, the isolated nature of data collected from disparate sources (referred to as 'Silos') presents significant challenges in multi-source data integration due to inherent heterogeneity and differences in data structures, formats, and standards. Domain adaptation emerges as a key framework to transition from 'Silos' to 'Synthesis' by measuring and mitigating such discrepancies, enabling uniform representation and harmonization of multi-source data.
This study explores different approaches to healthcare data integration, highlighting the challenges associated with each type and discussing both general-purpose and healthcare-specific adaptation methods. We examine key research challenges and evaluate leading domain adaptation approaches, demonstrating their effectiveness and limitations in advancing healthcare data integration.
The findings highlight the potential of domain adaptation methods to significantly improve healthcare data integration while laying a foundation for future research.
Current research often lacks a comprehensive analysis of how domain adaptation can effectively address the challenges associated with integrating multi-source and multi-modal healthcare datasets. This study serves as a valuable resource for healthcare professionals and researchers, providing guidance on leveraging domain adaptation techniques to mitigate domain discrepancies in healthcare data integration.
整合来自不同来源的数据不仅对于解决健康信息学中的数据稀缺问题至关重要，而且还能利用多个数据集的互补信息。然而，从不同来源收集的数据（称为“孤岛”）的孤立性质，由于固有的异质性以及数据结构、格式和标准的差异，在多源数据整合中带来了重大挑战。领域适应作为一个关键框架应运而生，通过测量和缓解此类差异，实现从“孤岛”到“综合”的转变，从而实现多源数据的统一表示和协调。
本研究探索了医疗数据整合的不同方法，强调了每种类型相关的挑战，并讨论了通用和特定于医疗保健的适应方法。我们研究了关键的研究挑战，并评估了领先的领域适应方法，展示了它们在推进医疗数据整合方面的有效性和局限性。
研究结果突出了领域适应方法在显著改善医疗数据整合方面的潜力，同时为未来研究奠定了基础。
当前的研究往往缺乏对领域适应如何有效应对与整合多源和多模态医疗数据集相关挑战的全面分析。本研究为医疗专业人员和研究人员提供了宝贵的资源，为利用领域适应技术减轻医疗数据整合中的领域差异提供了指导。
