Challenges and proposed solutions in modeling multimodal data: A systematic review
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912660389363712 |
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| author | Farhadizadeh, Maryam Weymann, Maria Blaß, Michael Kraus, Johann Gundler, Christopher Walter, Sebastian Hempen, Noah Binder, Harald Binder, Nadine |
| author_facet | Farhadizadeh, Maryam Weymann, Maria Blaß, Michael Kraus, Johann Gundler, Christopher Walter, Sebastian Hempen, Noah Binder, Harald Binder, Nadine |
| contents | Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06945 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Challenges and proposed solutions in modeling multimodal data: A systematic review Farhadizadeh, Maryam Weymann, Maria Blaß, Michael Kraus, Johann Gundler, Christopher Walter, Sebastian Hempen, Noah Binder, Harald Binder, Nadine Machine Learning Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications. |
| title | Challenges and proposed solutions in modeling multimodal data: A systematic review |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.06945 |