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

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DOI:

https://doi.org/10.18489/sacj.v35i2.17444

Keywords:

Ontology engineering, Ontology learning, Knowledge management, Knowledge base, SPARQL, Natural Language-Driven Dialogue System, Human-Computer interaction, MedRehabBot

Abstract

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, and
SPARQL 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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Published

2023-12-14

How to Cite

Kaverinsky, V., & Malakhov, K. (2023). Natural Language-Driven Dialogue Systems for Support in Physical Medicine and Rehabilitation. South African Computer Journal, 35(2). https://doi.org/10.18489/sacj.v35i2.17444

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