Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection

Abstract

Machine Learning (ML) and Deep Learning (DL) are derivatives of Artificial Intelligence (AI) that have already demonstrated their effectiveness in a variety of domains, including healthcare, where they are now routinely integrated into patients’ daily activities. On the other hand, data heterogeneity has long been a key obstacle in AI, ML and DL. Here, Multimodal Machine Learning (Multimodal ML) has emerged as a method that enables the training of complex ML and DL models that use heterogeneous data in their learning process. In addition, Multimodal ML enables the integration of multiple models in the search for a single, comprehensive solution to a complex problem. In this review, the technical aspects of Multimodal ML are discussed, including a definition of the technology and its technical underpinnings, especially data fusion. It also outlines the differences between this technology and others, such as Ensemble Learning, as well as the various workflows that can be followed in Multimodal ML. In addition, this article examines in depth the use of Multimodal ML in the detection and prediction of Cardiovascular Diseases, highlighting the results obtained so far and the possible starting points for improving its use in the aforementioned field. Finally, a number of the most common problems hindering the development of this technology and potential solutions that could be pursued in future studies are outlined.