# Digital Tools for Decision Support in Social Rehabilitation

## TL;DR

This paper introduces digital tools using semantic models and AI to support personalized decision-making in social rehabilitation processes.

## Contribution

The novelty lies in proposing interconnected semantic models and a methodology for virtual assistants in rehabilitation.

## Key findings

- A suite of semantic models was developed for decision support in rehabilitation.
- An ontological approach combined with the IACPaaS platform was proposed for knowledge representation.
- The methodology allows for personalized rehabilitation planning through updated regulatory and empirical data.

## Abstract

Objectives: The process of social rehabilitation involves several stages, from assessing an individual’s condition and determining their potential for rehabilitation to implementing a personalized plan with continuous monitoring of progress. Advances in information technology, including artificial intelligence, enable the use of software-assisted solutions for objective assessments and personalized rehabilitation strategies. The research aims to present interconnected semantic models that represent expandable knowledge in the field of rehabilitation, as well as an integrated framework and methodology for constructing virtual assistants and personalized decision support systems based on these models. Materials and Methods: The knowledge and data accumulated in these areas require special tools for their representation, access, and use. To develop a set of models that form the basis of decision support systems in rehabilitation, it is necessary to (1) analyze the domain, identify concepts and group them by type, and establish a set of resources that should contain knowledge for intellectual support; (2) create a set of semantic models to represent knowledge for the rehabilitation of patients. The ontological approach, combined with the cloud cover of the IACPaaS platform, has been proposed. Results: This paper presents a suite of semantic models and a methodology for implementing decision support systems capable of expanding rehabilitation knowledge through updated regulatory frameworks and empirical data. Conclusions: The potential advantage of such systems is the combination of the most relevant knowledge with a high degree of personalization in rehabilitation planning.