AI technologies for supporting clinical decision-making in the medical rehabilitation of stroke patients

Objective: To justify-both clinically and methodologically—the use of the developed set of rehabilitation ontologies and the formalized knowledge bases. This set of ontologies and knowledge bases supports clinical decision-making for stroke patients at all stages of rehabilitation.

Materials and methods. This study methodologically relies on an ontological approach to knowledge representation on the IACPaaS cloud platform. Structured knowledge bases were developed using clinical guidelines, rehabilitation standards, and the expertise of specialists from Vladivostok Clinical Hospital No. 1. Ontological models and knowledge bases were formalized using directed graphs in the IACPaaS environment which ensures independence between the population and updating of the knowledge base and the software code. Expert validation was conducted at every stage of development.

Results. A set of interconnected, formalized knowledge bases has been created and integrated into a cloud-based platform to support decision-making throughout the rehabilitation process. Since 2024, this system has been undergoing clinical trials in the medical rehabilitation department for neurological patients with central nervous system disorders at Vladivostok Clinical Hospital No. 1.

Conclusion. The proposed set of AI-based ontologies and knowledge bases offers formalized, intelligent support to multidisciplinary rehabilitation teams at every stage of patient management after a stroke. This set creates a methodological foundation for personalized rehabilitation that considers the requirements of the International Classification of Functioning (ICF) and clinical guidelines.

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