A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom
Article
Shaik, Thanveer, Tao, Xiaohui, Li, Lin, Xie, Haoran and Velasquez, Juan D.. 2024. "A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom." Information Fusion. 102. https://doi.org/10.1016/j.inffus.2023.102040
Article Title | A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom |
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ERA Journal ID | 20983 |
Article Category | Article |
Authors | Shaik, Thanveer, Tao, Xiaohui, Li, Lin, Xie, Haoran and Velasquez, Juan D. |
Journal Title | Information Fusion |
Journal Citation | 102 |
Article Number | 102040 |
Number of Pages | 18 |
Year | 2024 |
Publisher | Elsevier |
ISSN | 1566-2535 |
1872-6305 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.inffus.2023.102040 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1566253523003561 |
Abstract | Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data to information to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. We present a comprehensive review of multimodal medical data fusion focused on the integration of various data modalities. The review explores different approaches such as feature selection, rule-based systems, machine ;earning, deep learning, and natural language processing, for fusing and analyzing multimodal data. This paper also highlights the challenges associated with multimodal fusion in healthcare. By synthesizing the reviewed frameworks and theories, it proposes a generic framework for multimodal medical data fusion that aligns with the DIKW model. Moreover, it discusses future directions related to the four pillars of healthcare: Predictive, Preventive, Personalized, and Participatory approaches. The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare. Our findings can guide researchers and practitioners in leveraging the power of multimodal fusion with the state-of-the-art approaches to revolutionize healthcare and improve patient outcomes. |
Keywords | Data fusion; DIKW; Multimodality; p4 medicine; Smart healthcare |
Related Output | |
Is part of | Revolutionizing healthcare with federated reinforcement learning: from machine learning to machine unlearning |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460502. Data mining and knowledge discovery |
Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
Byline Affiliations | School of Mathematics, Physics and Computing |
Lingnan University of Hong Kong, China | |
Instituto Sistemas Complejos de Ingeniería (ISCI), Chile | |
University of Chile, Chile |
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