Cross-Lingual Mental Health Ontologies for Indian Languages: Bridging Patient Expression and Clinical Understanding through Explainable AI and Human-in-the-Loop Validation

AbstractMental 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 English or Western-centric diagnostic frameworks, leaving a gap in representing patient distress expressions in Indian languages. We propose the Cross-Lingual Graphs of Patient Distress 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, enabling more inclusive and patient-centric NLP tools for mental health care in multilingual contexts.