Artikel
Early Multimodal Data Integration for Data-Driven Medical Research – a Scoping Review
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Autoren
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Julia Gehrmann
- Universitätsklinikum Köln, Köln, Germany
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Oya Beyan
- Universität zu Köln, Köln, Germany
| Veröffentlicht: | 6. September 2024 |
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Gliederung
Text
Data-driven medical research (DDMR) needs multimodal data (MMD) to sufficiently capture the complexity of clinical cases. Methods for early multimodal data integration (MMDI), i.e. integration of the data before performing a data analysis, vary from basic concatenation to applying Deep Learning, each with distinct characteristics and challenges. Besides early MMDI, there exists late MMDI which performs modality-specific data analyses and then combines the analysis results. We conducted a scoping review, following PRISMA guidelines, to identify and analyze 21 articles reviewing methods for early MMDI between 2019 and 2024. Our analysis categorized these methods into four groups and summarized group-specific characteristics that are relevant for choosing the optimal method combination for MMDI pipelines in DDMR projects. Future research could focus on comparing early and late MMDI approaches as well as automating the optimization of MMDI pipelines to integrate vast amounts of real-world medical data effectively, facilitating holistic DDMR.
The authors declare that they have no competing interests.
The authors declare that an ethics committee vote is not required.
