August 24 · Review · Sensors · Cardiology and Cardiovascular Medicine
Cardiovascular disease remains the world's leading cause of mortality, yet everyday care still relies on episodic, symptom-driven interventions that detect ischemia, arrhythmias, and remodeling only after tissue damage has begun, limiting the effectiveness of therapy. A narrative review synthesized 183 studies published between 2016 and 2025 that were located through PubMed, MDPI, Scopus, IEEE Xplore, and Web of Science. This review examines CVD diagnostics using innovative technologies such as digital cardiovascular twins, which involve the collection of data from wearable IoT devices (electrocardiography (ECG), photoplethysmography (PPG), and mechanocardiography), clinical records, laboratory biomarkers, and genetic markers, as well as their integration with artificial intelligence (AI), including machine learning and deep learning, graph and transformer networks for interpreting multi-dimensional data streams and creating prognostic models, as well as generative AI, medical large language models (LLMs), and autonomous agents for decision support, personalized alerts, and treatment scenario modeling, and with cloud and edge computing for data processing. This multi-layered architecture enables the detection of silent pathologies long before clinical manifestations, transforming continuous observations into actionable recommendations and shifting cardiology from reactive treatment to predictive and preventive care. Evidence converges on four layers: sensors streaming multimodal clinical and environmental data; hybrid analytics that integrate hemodynamic models with deep-, graph- and transformer learning while Bayesian and Kalman filters manage uncertainty; decision support delivered by domain-tuned medical LLMs and autonomous agents; and prospective simulations that trial pacing or pharmacotherapy before bedside use, closing the prediction-intervention loop. This stack flags silent pathology weeks in advance and steers proactive personalized prevention. It also lays the groundwork for software-as-a-medical-device ecosystems and new regulatory guidance for trustworthy AI-enabled cardiovascular care.
ZBZhanel BaigarayevaOpen access
August 31 · Article · Вестник КазАТК · Control and Systems Engineering
The study is devoted to the application of the NSGA-III multi-criteria optimization algorithm to solve marketing and advertising problems in the agro-industrial complex (AIC) of Kazakhstan. In the context of increasing competition and digitalization of the economy of the Republic of Kazakhstan, the relevance of the work is dictated by the need to optimize the distribution of marketing budgets of enterprises, taking into account such criteria as the effectiveness of advertising campaigns, coverage of the target audience and cost minimization. The methodology used in the article included the formalization of target functions, constraints and the use of NSGA-III to search for Pareto-optimal solutions. The results obtained during cybernetic modeling generally demonstrate the high quality of solutions (hyper volume), a compromise between the criteria and the dominant role of digital channels (up to 39.8% of the budget). The data obtained during the research confirm the potential applicability of NSGA-III for marketing planning tasks in the agro-industrial complex and can serve as a basis for management decisions at agro-industrial complex enterprises.
GBGulzhanat BeketovaOpen access
August 31 · Article · Journal of Health Development · Epidemiology
This review presents the of digital epidemiology, which based on the use of digital data and analytical methods to detect, analyze, and prevent outbreaks of infections. Digital epidemiology can start early investigation, diagnosis, and response measures at the regional level, and it is a useful approach to traditional surveillance in the modern world. The article provides an overview of early warning systems, including ProMed-mail, HealthMap, EIOS, and MedISys, highlighting their effective application in the early detection of disease outbreaks through digital tools. The GLEAM project provides analytical and forecasting tools using the complex stochastic models of infection spread based on big data. Countries such as South Korea, Singapore, Taiwan, and Germany have demonstrated the effectiveness of digital epidemiology approach within the context of government initiatives that applied non-traditional medical data and digital solutions to manage disease outbreaks. The research methodology blends specialized techniques, such as a literature review, comparative analysis, and SWOT analysis. A comparative table of early warning systems highlights the advantages of systems that use big data from open sources with minimal human intervention. The SWOT analysis suggests that digital epidemiology has potential for implementation in Kazakhstan, raising the bar for epidemiological surveillance and monitoring. The study aims to analyze modern approaches to epidemiological surveillance and monitoring of infectious diseases and assess the prospects for implementation in the healthcare system in Kazakhstan.
ZSZhanat SaduakassovaOpen access
