OPTIMIZING PERFORMANCE OF A MULTIDISCIPLINARY REHABILITATION TEAM IN CARE DELIVERY TO STROKE SURVIVORS USING ARTIFICIAL INTELLIGENCE METHODS
Authors:   Institute of Automation and Control Processes Far Eastern Branch of the Russian Academy of Sciences: Vladivostok, Russia, Gribova Valeriya , Shestopalov Yevgeniy ,   Pacific State Medical University: Vladivostok, Russia, Lebedev Sergei ,   Pacific State Medical University: Vladivostok, Russia, Shalfeeva Elena ,   Institute of Automation and Control Processes Far Eastern Branch of the Russian Academy of Sciences: Vladivostok, Russia, Okun Dmitry ,   Institute of Automation and Control Processes Far Eastern Branch of the Russian Academy of Sciences: Vladivostok, Russia, Kovalev Roman ,   Institute of Automation and Control Processes Far Eastern Branch of the Russian Academy of Sciences: Vladivostok, Russia, Shepeta Ekaterina ,   Pacific State Medical University: Vladivostok, Russia, Fedorischev Leonid ,   Institute of Automation and Control Processes Far Eastern Branch of the Russian Academy of Sciences: Vladivostok, Russia, Lifshitc Aleksandr ,   Institute of Automation and Control Processes Far Eastern Branch of the Russian Academy of Sciences: Vladivostok, Russia
Published: August 2024

Significance. At the present stage of the rehabilitation medicine development, it has become clear that decision support systems and artificial intelligence technologies should be actively implemented. These technologies can assist specialists in gaining a better understanding of impairments, activity levels, and engagement of individuals with stroke in rehabilitation. The creation of a unified tool will aid the multidisciplinary team in formulating a rehabilitation diagnosis and determining a more accurate rehabilitation potential, thereby ensuring high overall effectiveness of medical rehabilitation The purpose of the study was to develop a decision support system for a multidisciplinary rehabilitation team at the diagnostic stage, based on the International Classification of Functioning, Disability and Health, to set rehabilitation goals for stroke survivors. Material and methods. This study used various artificial intelligence methods to develop an intelligent service, including system analysis, ontological engineering, graph theory, and knowledge engineering. Cloud technologies and an object-oriented programming approach were employed in the implementation. Results. To implement the intelligent service, a knowledge structure about diagnosis of the condition and needs of stroke survivors were defined. A role structure of each member of the rehabilitation team was determined, and a set of information and software components were developed. Knowledge digraphs were created, providing comprehensive representations of diagnostic methods and techniques, as well as overall rehabilitation strategies. Corresponding roles and functions were assigned to each member of the rehabilitation team. Conclusion The developed intelligent Clinical Decision Support System in the field of stroke survivor rehabilitation is based on expert knowledge, an ontological approach, and a knowledge representation model in the form of digraphs. Knowledge systematization and formalization within the system improve the quality of diagnosis in the terms of the International Classification of Functioning, Disability, and Health. The information and software components of the Clinical Decision Support System ensure its effective functioning within various clinical scenarios and with different data sets. The system provides medical specialists with up-to-date information and recommendations based on scientific research and expert knowledge, aiding in decision-making and selecting optimal rehabilitation strategies for each patient. A personalized approach and precise data analysis for a comprehensive diagnosis will optimize the rehabilitation process, reducing recovery time and increasing patient satisfaction.