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📄 Abstract
Abstract: Mental health communication in India is linguistically fragmented, culturally
diverse, and often underrepresented in clinical NLP. Current health ontologies
and mental health resources are dominated by diagnostic frameworks centered on
English or Western culture, leaving a gap in representing patient distress
expressions in Indian languages. We propose cross-linguistic graphs of patient
stress expressions (CL-PDE), a framework for building cross-lingual mental
health ontologies through graph-based methods that capture culturally embedded
expressions of distress, align them across languages, and link them with
clinical terminology. Our approach addresses critical gaps in healthcare
communication by grounding AI systems in culturally valid representations,
allowing more inclusive and patient-centric NLP tools for mental health care in
multilingual contexts.
Key Contributions
This paper proposes cross-linguistic graphs of patient stress expressions (CL-PDE), a framework for building cross-lingual mental health ontologies for Indian languages. It uses graph-based methods and human-in-the-loop validation with explainable AI to capture culturally embedded expressions, align them across languages, and link them to clinical terms, addressing critical gaps in mental healthcare communication.
Business Value
Enhances mental healthcare accessibility and quality for diverse linguistic populations in India by enabling culturally sensitive AI tools, potentially reducing disparities and improving patient outcomes.