Multimodal machine learning models improved ischemic heart disease risk prediction with an AUC of 0.86 and AUCPR of 0.70 when combining clinical and imaging data.
Does multimodal data fusion using machine learning improve cardiovascular disease diagnosis and risk prediction compared to single-modality approaches?
Integrating diverse data sources such as imaging, EMR, and genetics through machine learning data fusion enhances the accuracy of cardiovascular risk prediction and diagnosis.
p-value: p=<0.05
Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized and timely. Sources of data for digital health tools include multiple modalities such as electronic medical records (EMR), radiology images, and genetic repositories, to name a few. While historically, these data were utilized in silos, new machine learning (ML) and deep learning (DL) technologies enable the integration of these data sources to produce multi-modal insights. Data fusion, which integrates data from multiple modalities using ML and DL techniques, has been of growing interest in its application to medicine. In this paper, we review the state-of-the-art research that focuses on how the latest techniques in data fusion are providing scientific and clinical insights specific to the field of cardiovascular medicine. With these new data fusion capabilities, clinicians and researchers alike will advance the diagnosis and treatment of cardiovascular diseases (CVD) to deliver more timely, accurate, and precise patient care.
Amal et al. (Wed,) conducted a review in Cardiovascular Disease. Multi-Modal Data and Machine Learning was evaluated on Risk assessment for ischemic heart disease (IHD) (p=<0.05). Multimodal machine learning models improved ischemic heart disease risk prediction with an AUC of 0.86 and AUCPR of 0.70 when combining clinical and imaging data.