Alina Vozna

This paper presents an Answer Set Programming (ASP)-based framework for medical appointment scheduling, aimed at improving efficiency, reducing administrative overhead, and enhancing patient-centered care. The framework personalizes scheduling for vulnerable populations by integrating Blueprint Personas. It ensures real-time availability updates, conflict-free assignments, and seamless interoperability with existing healthcare platforms by centralizing planning operations within an ASP logic model.

This paper presents a trust-aware architecture for personalized digital health that combines user modeling, symbolic reasoning, and adaptive trust mechanisms. The proposed system uses Blueprint Personas to capture detailed patient profiles, including clinical, behavioral, and emotional traits. These profiles guide an intelligent agent that interacts with patients and healthcare professionals to provide context-sensitive support. Personalization is achieved through an ontology-based reasoning layer that interprets user needs and integrates real-time data from electronic health records, wearable devices, and environmental sources. To promote transparency and foster long-term user engagement, the system includes a formal trust modeling component based on a Reference Ontology of Trust (ROT), allowing the system to flexibly tailor communication strategies in response to user feedback and evolving trust levels. A simulated scenario involving a patient with chronic obstructive pulmonary disease demonstrates how the system delivers proactive and personalized healthcare interventions, such as medication reminders and air quality alerts. While the architecture is modular and designed for scalability, it has not yet been deployed in real-world clinical settings. Empirical validation and integration with clinical platforms remain part of future work. Nevertheless, this ongoing work contributes to the development of explainable and ethically aligned AI systems that enhance autonomy, accessibility, and trust in digital health environments through explainable reasoning.