The article “Development of Mobile System for Medical Recommendations” addresses the challenges in creating mobile applications that provide medical recommendations. It introduces a method for selecting components to construct text templates, defines text block patterns using Backus-Naur Form (BNF) notation, and presents an algorithm for detecting fuzzy duplicates in natural language texts. Additionally, the paper develops methods for forming new patterns absent in the pattern database and extracting pragmatic signs from semi-structured natural language texts. The authors conclude by discussing the modeling and development of a mobile system for medical recommendations.
Takeaways:
- The study emphasizes the importance of structured text representation and pattern recognition in developing effective medical recommendation systems.
- It highlights the role of natural language processing techniques, such as fuzzy duplicate detection and pragmatic sign extraction, in enhancing the accuracy and relevance of medical recommendations.
- The research contributes to the field by proposing methodologies for constructing and expanding pattern databases, which are crucial for personalized medical advice.
- The development of a mobile system demonstrates the practical application of these methodologies, aiming to improve user engagement and accessibility to medical recommendations.
