Cross-Lingual Mental Health Ontologies for Indian Languages: Bridging Patient Expression and Clinical Understanding through Explainable AI and Human-in-the-Loop Validation
This work addresses the gap in culturally valid representations for mental health care in multilingual contexts, enabling more inclusive and patient-centric NLP tools, though it appears incremental as it builds on existing ontology and graph methods.
The paper tackled the problem of mental health communication in India being linguistically fragmented and culturally underrepresented in clinical NLP by proposing a framework for building cross-lingual mental health ontologies using graph-based methods to align patient distress expressions across languages with clinical terminology.
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.