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<p>Multimodal data fusion in healthcare platforms aligns with the principles of predictive, preventive, and personalized medicine (3PM) by harnessing the power of diverse data sources. The integrated approach enables predictive modeling, preventative interventions, and personalized healthcare strategies which result in better patient outcomes and more effective delivery of healthcare. This paper presents a paradigm shift in healthcare from reactive to predictive strategies, emphasizing 3PM. The integration of cutting-edge diagnostics, targeted prevention, and individualized care holds vast potential. In this transformation, the multimodal data from wearables and sensors become central. In the context of healthcare platforms, this paper discusses data collection, preprocessing, and fusion challenges arising from a diverse data landscape. The use of big data analysis, machine learning, and artificial intelligence is emphasized. The analysis extends beyond data collection, encompassing proposed data processing solutions, fusion methodologies, and the implementation of deep multimodal data analysis networks.</p>

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An overview of methods and techniques in multimodal data fusion with application to healthcare

  • 期刊论文

  • 发表日期：

    2025年1月10日

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  • Siwar Chaabene,
  • Amal Boudaya,
  • Bassem Bouaziz,
  • Lotfi Chaari

摘要

Multimodal data fusion in healthcare platforms aligns with the principles of predictive, preventive, and personalized medicine (3PM) by harnessing the power of diverse data sources. The integrated approach enables predictive modeling, preventative interventions, and personalized healthcare strategies which result in better patient outcomes and more effective delivery of healthcare. This paper presents a paradigm shift in healthcare from reactive to predictive strategies, emphasizing 3PM. The integration of cutting-edge diagnostics, targeted prevention, and individualized care holds vast potential. In this transformation, the multimodal data from wearables and sensors become central. In the context of healthcare platforms, this paper discusses data collection, preprocessing, and fusion challenges arising from a diverse data landscape. The use of big data analysis, machine learning, and artificial intelligence is emphasized. The analysis extends beyond data collection, encompassing proposed data processing solutions, fusion methodologies, and the implementation of deep multimodal data analysis networks.