System interprets natural language queries to retrieve autism-related info from a database.

What is it about?

Goal: To create a system that allows users to ask questions about Autism Spectrum Disorder (ASD) in natural language (like everyday English) and get relevant information extracted from a specialized database. Core Function: Input: Users type or speak questions about ASD (e.g., "What are the early signs of autism?", "List therapies for nonverbal children"). Processing: The system uses Natural Language Processing (NLP) techniques to understand the user's question. Output: It retrieves and presents specific information about ASD sourced from a structured database. Focus: Making access to ASD information easier and more intuitive by allowing users to ask questions conversationally, without needing technical database querying skills or knowing specific keywords. Key Difference from the Previous Paper: While the other paper focused on detecting diseases (like diagnosing) from Bengali symptom descriptions, this paper focuses on retrieving factual information about one specific disorder (ASD) from a database using natural language queries.

Featured Image

Why is it important?

1. Accessibility for Non-Technical Users It bridges the gap between non-technical users (e.g., caregivers, parents, and clinicians) and complex medical databases. By allowing natural language queries (like “What are early signs of ASD?”), it makes critical autism-related information more accessible. 2. Domain-Specific Intelligence in ASD Autism Spectrum Disorder (ASD) is a nuanced field with complex terminology. This system interprets and processes Bengali or English language queries, converting them into structured database queries (like SQL), specifically aimed at extracting ASD-relevant information — a crucial innovation for public health communication. 3. Natural Language Processing (NLP) Innovation The work showcases the practical application of NLP techniques (such as entity recognition, intent detection, query classification) in a domain-specific scenario, advancing the field of semantic search and question answering systems. 4. Regional Language Support By possibly supporting regional languages (e.g., Bengali), it promotes digital inclusivity, allowing broader segments of the population in underrepresented areas to access and benefit from autism-related health information. 5. Assistive Tool in Healthcare and Policy Such a system could serve as an assistive tool for healthcare professionals, aiding in quick access to database-driven knowledge for early screening, intervention strategies, or even guiding policy-level decisions in ASD healthcare management. 6. Foundation for Future Systems This work lays the groundwork for building more intelligent, multilingual health information systems using advanced NLP models, which could be expanded to other neurodevelopmental disorders and broader medical contexts.

Perspectives