A COMPREHENSIVE MULTI-MODAL FRAMEWORK FOR PREDICTING AND MANAGING CONGESTIVE HEART FAILURE
Abstract
Congestive Heart Failure (CHF) remains one of the most prevalent and life-threatening cardiovascular conditions globally, demanding highly accurate diagnostic and predictive tools. This paper proposes a comprehensive multi-modal framework that synthesizes electrocardiography (ECG) signals, electronic health records (EHR), and medical imaging to predict CHF onset, risk of readmission, and potential treatment complications. While previous research has successfully leveraged isolated data streams, clinical realities necessitate a unified approach to patient monitoring and proactive care. By integrating advanced temporal sequence modeling, entropic signal analysis, and natural language processing for drug interaction screening, our proposed methodology aims to deliver a holistic diagnostic pipeline. We detail the foundational related literature across signal processing, longitudinal predictive modeling, and automated literature analysis to contextualize our contributions. Furthermore, we outline a structured methodology comprising data ingestion, feature fusion, and clinical evaluation protocols. Finally, we discuss the practical implications of deploying such a multi-modal system, alongside its critical limitations, ethical considerations, and vital directions for future research.
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