Unified modeling language for patient-centered telerehabilitation: A comprehensive framework integrating medical and biopsychosocial pathways

BACKGROUND

Effective telerehabilitation requires robust, standardized models to ensure comprehensive and continuous patient monitoring. However, existing rehabilitation models often lack integration, failing to cover the entire care continuum and its interdisciplinary aspects. This gap limits their applicability in real-world settings.

OBJECTIVE

This study introduces a semi-formal, Unified Modeling Language (UML)-based framework that provides a holistic, patient-centered representation of the rehabilitation pathway. The model is designed to bridge gaps in care coordination, aligning with recent scientific advances and healthcare policies emphasizing patient empowerment and interdisciplinary collaboration.

METHODS

Using a professional didactics approach, we conducted a literature review, field observations, and expert consultations (questionnaires, interviews) to map rehabilitation pathways across diverse conditions and settings. The model was iteratively refined based on expert feedback to ensure its accuracy and usability.

RESULTS

Our findings reveal significant fragmentation in rehabilitation pathways, driven by diverse clinical practices and discontinuities in care. To address this, the proposed UML-based model integrates medical, functional, psychosocial, and organizational data, ensuring a cohesive, capability-driven approach. The structured design enhances communication between stakeholders and improves interoperability across healthcare systems.

CONCLUSION

The proposed model provides a scalable foundation for digital telerehabilitation solutions, adaptable to various healthcare environments. By facilitating data integration and standardization, it supports better patient monitoring, decision-making, and personalized rehabilitation strategies. Future research will focus on refining the model to incorporate specialized rehabilitation fields and enhance interoperability with existing medical information systems.

Agent-based approaches for biological modeling in oncology: A literature review

CONTEXT

Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions.

METHODOLOGY

This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023.

RESULTS

Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.