Multimodal machine learning models demonstrated high accuracy for heart failure classification, achieving a pooled AUROC of 92.83, and generally outperformed unimodal approaches.
Systematic Review
Do multimodal machine learning models improve heart failure classification and prognosis prediction compared to unimodal approaches?
Multimodal machine learning models show high accuracy for heart failure classification and prognosis, outperforming unimodal models, though clinical adoption is currently limited by a lack of external validation and prospective studies.
Effect estimate: Pooled AUROC 92.83 (95% CI 88.91-96.73)
p-value: p=<0.001
Introduction Heart failure (HF) is a global medical condition marked by substantial morbidity, mortality, and healthcare costs with complex pathophysiology and variation in definitions. Machine learning (ML) has emerged as a promising approach to improve HF classification and risk prediction by leveraging various data sources. This study aims to present the current state-of-the-art multimodal ML models for HF classification and prognosis prediction, focusing on their modalities, performance, and clinical utility. Methods Following PRISMA guidelines and registered with PROSPERO (CRD420250654631), this review searched across four electronic databases (November 2014 – November 2024) and identified 284 unique records, of which 15 were included in the final synthesis. The quality of the studies was evaluated using QUADAS-2 and QUAPAS. Results Our results showed that the two most common multimodal combinations were tabular-image and tabular-text. The algorithms of the models included convolutional neural networks for image data, transformer-based approaches for text, with well-known fused techniques (early, middle, late fusion). Overall, multimodal models demonstrated superior performance compared to unimodal approaches, achieving area under the receiver operating characteristic curve values frequently exceeding 80% and reaching as high as 98.2%. Conclusion Despite promising results, challenges include inconsistent reporting of performance metrics and their 95% confidence intervals, limited external validation, a near absence of prospective studies, and a deficiency in integrating genetic or 'omics' information with conventional data. These challenges must be addressed to promote clinical adoption and future research Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420250654631 , identifier CRD420250654631.
Hoang et al. (Wed,) conducted a systematic review in Heart failure. Multimodal machine learning models vs. Unimodal machine learning models was evaluated on Heart failure classification (Pooled AUROC) (Pooled AUROC 92.83, 95% CI 88.91-96.73, p=<0.001). Multimodal machine learning models demonstrated high accuracy for heart failure classification, achieving a pooled AUROC of 92.83, and generally outperformed unimodal approaches.