Data-Driven Clinical Decision Support Systems for Optimizing Treatment Pathways in Cardiovascular Diseases

Abstract

Cardiovascular diseases (CVDs) are still the primary cause of morbidity and mortality worldwide, thus, treatment strategies should be fast, precise and patientcentered. This study proposes the idea of having a Clinical Decision Support System (CDSS) which employs advanced machine learning (ML) and deep learning (DL) models which can determine the best ways to treat cardiovascular diseases based on data. The method integrates various forms of cardiovascular data sets, such as electronic health records, imaging datasets and lab data, into an ordered chain that comprises of data preparation, extraction of multimodal features and making predictions. The suggested approach involves prediction analytics to propose the path of treatment, though there is a focus on personalized suggestions which are specific to the patient's personality or any other condition they may have. Case studies demonstrate how the framework can be used to & improve treatment accuracy, route efficiency, and total patient results in acute coronary syndrome and heart failure care. The main idea of this paper is data-driven CDSS has the ability to completely change precision cardiology and help doctors give fast, personalized and result-oriented treatments. In the end, the study helps in bridging the gap between the raw healthcare data and useful insights.

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