2024
Chapter
Cardiovascular diseases (CVD) are the leading cause of death in most countries around the world, making the accurate assessment of risks and the selection of individual preventive strategies a current focus in healthcare. In this article, the authors presented a prototype of a Clinical Decision Support System (CDSS) for predicting and preventing cardiovascular risks based on a hybrid architecture that integrates machine learning models and ontological knowledge bases. A microservice architecture based on the Cloud-Edge approach is proposed for optimizing computational resources when processing tabular data, signals, video, and images, as well as for enhancing the effectiveness of integration with various Healthcare Information Systems (HIS). The CDSS supports the formalization not only of medical history data and results of studies but also the rules for interpreting the results of predictions based on machine learning models and methods of explainable artificial intelligence (XAI). The developed CDSS includes widely used tools in clinical cardiology and cardiothoracic surgery for risk assessment, as well as proprietary machine learning models for predicting in-hospital mortality, and others. These models contribute to making informed medical decisions for the diagnosis, prevention, and treatment of CVD. The prototype was implemented at the Medical Center of the Far Eastern Federal University and integrated with the "1C" HIS. The experience of implementing the prototype demonstrated the high potential of hybrid CDSS based on microservice architecture for use in clinical practice.