Hybrid e-rehabilitation services:  SMART-system for remote support of rehabilitation activities and services

Palagin, Oleksandr; Malakhov, Kyrylo; Velychko, Vitalii; Semykopna, Tetiana V.

doi:10.5195/ijt.2022.6480

Cited by 11 publications

(15 citation statements)

References 8 publications

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“…This paper introduces a comprehensive framework for the natural language-driven dialogue system, meticulously designed to offer vital support in the physical medicine and rehabilitation domain. Our holistic approach is an amalgamation of diverse disciplines such as intelligent information systems, data mining, ontologies, and human-computer interaction, culminating in a groundbreaking tool tailored for both healthcare professionals and students in the physical medicine and rehabilitation field (Malakhov, 2023a;Palagin, Malakhov, Velychko, Semykopna & Shchurov, 2022). Central to our system is the knowledge base formation process.…”

Section: Discussionmentioning

confidence: 99%

Natural Language-Driven Dialogue Systems for Support in Physical Medicine and Rehabilitation

Kaverinsky,

Malakhov

2023

S AFR COMP J

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This paper presents a natural language-driven dialogue system designed to support healthcare professionals and students in the field of physical medicine and rehabilitation. The system seamlessly integrates concepts from intelligent information systems, data mining, ontologies, and human-computer interaction, employing at its core a rule-based dialogue mechanism. The system harnesses the power of ontology-based graph knowledge, underscoring its domain-specific efficacy. This article delves into the automated knowledge base formation, utilising Python scripts to translate EBSCO’s dataset of articles on physical medicine and rehabilitation into an OWL ontology. This methodology ensures adaptability to the ever-evolving landscape of medical insights. The system’s approach to natural language processing encompasses text preprocessing, semantic category discernment, andSPARQL query creation, providing 26 predefined categories. As an innovation in performance optimisation, the system integrates a strategy to cache precomputed responses using a PostgreSQL database, which aids in resource conservation and reduction in query execution latency. The system’s user engagement avenues are further detailed, showcasing a Telegram bot and an API, enhancing accessibility and user experience. In essence, this article illuminates an advanced, efficient dialogue system for physical medicine and rehabilitation, synthesising multiple computational paradigms, and standing as a beacon for healthcare practitioners and students alike.

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Section: Discussionmentioning

confidence: 99%

Natural Language-Driven Dialogue Systems for Support in Physical Medicine and Rehabilitation

Kaverinsky,

Malakhov

2023

S AFR COMP J

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“…The meta-ontology, terms/contexts ontology, SPARQL queries to meta-ontology, samples of structured JSON prompts for the ChatGPT, test questions and results are publicly available via public GitHub repository 5 .…”

Section: Data Availabilitymentioning

confidence: 99%

“…The software alpha version of these services is also available for further exploration and evaluation. 5 https://github.com/knowledge-ukraine/OntoChatGPT Please reach out to our team to request access and to learn more about the functionalities and features of OntoChatGPT.…”

Section: Data Availabilitymentioning

confidence: 99%

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OntoChatGPT Information System: Ontology-Driven Structured Prompts for ChatGPT Meta-Learning

Палагин¹,

Kaverinskiy²,

Litvin³

et al. 2023

IJC

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This research presents a comprehensive methodology for utilizing an ontology-driven structured prompts system in interplay with ChatGPT, a widely used large language model (LLM). The study develops formal models, both information and functional, and establishes the methodological foundations for integrating ontology-driven prompts with ChatGPT’s meta-learning capabilities. The resulting productive triad comprises the methodological foundations, advanced information technology, and the OntoChatGPT system, which collectively enhance the effectiveness and performance of chatbot systems. The implementation of this technology is demonstrated using the Ukrainian language within the domain of rehabilitation. By applying the proposed methodology, the OntoChatGPT system effectively extracts entities from contexts, classifies them, and generates relevant responses. The study highlights the versatility of the methodology, emphasizing its applicability not only to ChatGPT but also to other chatbot systems based on LLMs, such as Google’s Bard utilizing the PaLM 2 LLM. The underlying principles of meta-learning, structured prompts, and ontology-driven information retrieval form the core of the proposed methodology, enabling their adaptation and utilization in various LLM-based systems. This versatile approach opens up new possibilities for NLP and dialogue systems, empowering developers to enhance the performance and functionality of chatbot systems across different domains and languages.

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“…Development of the cloud-based platform for patient-centered telerehabilitation of oncology patients with mathematical-related modeling (Malakhov, 2022(Malakhov, , 2023a(Malakhov, , 2023bPalagin, Malakhov, Velychko, Semykopna & Shchurov, 2022;Stetsyuk et al, 2023).…”

Section: Acknowledgementsmentioning

confidence: 99%

Ontology-Driven Computer Systems

Petrenko,

Cohn,

Shchurov

et al. 2023

S AFR COMP J

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This article delves into the evolving frontier of ontology-driven natural language information processing. Through an in-depth examination, we put forth a novel linguistic processor architecture, uniquely integrating linguistic and ontological paradigms during semantic analysis. Distancing from conventional methodologies, our approach showcases a profound merger of knowledge extraction and representation techniques. A central highlight of our research is the development of an ontology-driven information system, architected with an innate emphasis on self-enhancement and adaptability. The system’s salient capability lies in its adept handling of elementary knowledge, combined with its dynamic aptitude to foster innovative concepts and relationships. A particular focus is accorded to the system’s application in scientific information processing, signifying its potential in revolutionising knowledge-based applications within scientific domains. Through our endeavours, we aim to pave theway for more intuitive, precise, and expansive ontology-driven tools in the realm of knowledge extraction and representation.

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Hybrid e-rehabilitation services: SMART-system for remote support of rehabilitation activities and services

Cited by 11 publications

References 8 publications

Natural Language-Driven Dialogue Systems for Support in Physical Medicine and Rehabilitation

Natural Language-Driven Dialogue Systems for Support in Physical Medicine and Rehabilitation

OntoChatGPT Information System: Ontology-Driven Structured Prompts for ChatGPT Meta-Learning

Ontology-Driven Computer Systems

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