Purpose. This article aims to provide an in-depth examination of the digital health system of Ukraine, focusing on the emerging trends, precise definitions, established standards, and recent legislative revisions that shape the practice and implementation of eHealth solutions within the country. Background. The digital health landscape in Ukraine has witnessed significant transformations, especially in the wake of the COVID-19 pandemic and subsequent military conflicts. These events have catalyzed the expansion of telemedicine services, leading to innovative approaches in healthcare delivery. The national strategy underscores the necessity for human-centric and accessible telemedicine, reinforced by technological neutrality, and harmonization with global standards. Methods. A review of the current literature, national strategies, and legal documents was conducted, alongside an analysis of data usage and service provision patterns in various Ukrainian regions. Participation in the "Science for Safety and Sustainable Development of Ukraine" competition facilitated project initiatives like the development of a cloud-based platform for patient-centered telerehabilitation for oncology patients. Findings. The utilization of telemedicine has significantly increased in conflict-affected regions, demonstrating the need for, and the effective deployment of, digital health strategies under crisis conditions. Private health facilities and entrepreneurs have been pivotal in the provision of telemedicine services. Legislative efforts have been geared toward framing telemedicine as an integral component of the national eHealth system, ensuring interoperability, and aligning with international standards and the Internet of Medical Things (IoMT). Interpretation. The findings underscore the resilience and adaptability of the Ukrainian healthcare system in the face of adversity. There is a clear trend towards a more integrated, patient-focused, and technologically advanced healthcare model, aligning with international trends and prioritizing public health goals over private profits. This progress, however, is contingent upon continuous development, investment in technological infrastructure, and legislative support to sustain and advance digital health initiatives.

This research presents a comprehensive methodology for utilizing an ontology-driven structured prompts system in interplay with ChatGPT, a widely used large language model (LLM). The study develops formal models, both information and functional, and establishes the methodological foundations for integrating ontology-driven prompts with ChatGPT’s meta-learning capabilities. The resulting productive triad comprises the methodological foundations, advanced information technology, and the OntoChatGPT system, which collectively enhance the effectiveness and performance of chatbot systems. The implementation of this technology is demonstrated using the Ukrainian language within the domain of rehabilitation. By applying the proposed methodology, the OntoChatGPT system effectively extracts entities from contexts, classifies them, and generates relevant responses. The study highlights the versatility of the methodology, emphasizing its applicability not only to ChatGPT but also to other chatbot systems based on LLMs, such as Google’s Bard utilizing the PaLM 2 LLM. The underlying principles of meta-learning, structured prompts, and ontology-driven information retrieval form the core of the proposed methodology, enabling their adaptation and utilization in various LLM-based systems. This versatile approach opens up new possibilities for NLP and dialogue systems, empowering developers to enhance the performance and functionality of chatbot systems across different domains and languages.

The development of theories, methods, and algorithms for the discovery and formation of new knowledge remains one of the most important tasks for any researcher, especially if they are actively working to create new scientific publications. Yet, there is no universal language to describe full formal concepts (i.e. knowledge) or the systemology of transdisciplinary scientific research. Because of this, researchers face a set of urgent challenges. One such challenge is how to speed up the process of finding information in their own sources. To address this challenge, we created an ontology-related system for processing digital libraries of scientific publications. This system implements the technologies of information retrieval and knowledge discovery in digital libraries with an emphasis on technologies and instruments such as those used in the Semantic Web and cognitive graphics.

One of the most effective solutions in medical rehabilitation assistance is remote patient / person-centered rehabilitation. Rehabilitation also needs effective methods for the “Physical therapist – Patient – Multidisciplinary team” system, including the statistical processing of large volumes of data. Therefore, along with the traditional means of rehabilitation, as part of the “Transdisciplinary intelligent information and analytical system for the rehabilitation processes support in a pandemic (TISP)” in this paper, we introduce and define: the basic concepts of the new hybrid e-rehabilitation notion and its fundamental foundations; the formalization concept of the new Smart-system for remote support of rehabilitation activities and services; and the methodological foundations for the use of services (UkrVectores and vHealth) of the remote Patient / Person-centered Smart-system. The software implementation of the services of the Smart-system has been developed.

This Letter to the Editor provides an update on the research from the Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine. The Institute’s research team in collaboration with Ternopil National Medical University began a new project called “Development of the cloud-based platform for patient-centered telerehabilitation of oncology patients with mathematical-related modeling.” The project is dedicated to the development of a hybrid cloud-based platform, and the creation on its basis of information technology for the telemedicine rehabilitation of cancer patients, and adapted for patients with combat stress disorder. The distinctive features of the proposed technology are a combination of artificial intelligence methods with accurate mathematical methods for optimization: developing mathematical models of problems of discrete, and non-smooth optimization, subgradient space transformation algorithms (to minimize non-smooth functions with tens of thousands of variables), and a method of global equilibrium search, etc.

This study explores the potential of unsupervised machine learning algorithms to identify latent cardiac risk profiles by analyzing ECG-derived parameters from two general groups: clinically healthy individuals (Norm dataset, n = 14,863) and patients hospitalized with heart failure (patients’ dataset, n = 8220). Each dataset includes 153 ECG and heart rate variability (HRV) features, including both conventional and novel diagnostic parameters obtained using a Universal Scoring System. The study aims to apply unsupervised clustering algorithms to ECG data to detect latent risk profiles related to heart failure, based on distinctive ECG features. The focus is on identifying patterns that correlate with cardiac health risks, potentially aiding in early detection and personalized care. We applied a combination of Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and Hierarchical Density-Based Spatial Clustering (HDBSCAN) for unsupervised clustering. Models trained on one dataset were applied to the other to explore structural differences and detect latent predispositions to cardiac disorders. Both Euclidean and Manhattan distance metrics were evaluated. Features such as the QRS angle in the frontal plane, Detrended Fluctuation Analysis (DFA), High-Frequency power (HF), and others were analyzed for their ability to distinguish different patient clusters. In the Norm dataset, Euclidean distance clustering identified two main clusters, with Cluster 0 indicating a lower risk of heart failure. Key discriminative features included the “ALPHA QRS ANGLE IN THE FRONTAL PLANE” and DFA. In the patients’ dataset, three clusters emerged, with Cluster 1 identified as potentially high-risk. Manhattan distance clustering provided additional insights, highlighting features like “ST DISLOCATION” and “T AMP NORMALIZED” as significant for distinguishing between clusters. The analysis revealed distinct clusters that correspond to varying levels of heart failure risk. In the Norm dataset, two main clusters were identified, with one associated with a lower risk profile. In the patients’ dataset, a three-cluster structure emerged, with one subgroup displaying markedly elevated risk indicators such as high-frequency power (HF) and altered QRS angle values. Cross-dataset clustering confirmed consistent feature shifts between groups. These findings demonstrate the feasibility of ECG-based unsupervised clustering for early risk stratification. The results offer a non-invasive tool for personalized cardiac monitoring and merit further clinical validation. These findings emphasize the potential for clustering techniques to contribute to early heart failure detection and personalized monitoring. Future research should aim to validate these results in other populations and integrate these methods into clinical decision-making frameworks.