构建跨语言心理压力表达图谱，提升印度多语种心理健康沟通的准确性
Cross-Lingual Mental Health Ontologies for Indian Languages: Bridging Patient Expression and Clinical Understanding through Explainable AI and Human-in-the-Loop Validation
- 基于图结构建模跨语言患者压力表达，融合文化语境
- 将本土化表达与临床术语对齐，实现多语言语义贯通
- 结合可解释AI与人工验证，适合医疗AI本地化研究者
印度心理健康沟通存在语言割裂、文化多元等问题，现有健康本体和资源多以英语或西方诊断框架为主，难以反映印地语等本土语言中患者的痛苦表达。本文提出跨语言患者压力表达图谱（CL-PDE）框架，通过图方法捕捉文化嵌入的压力表达，实现跨语言对齐，并与临床术语关联。该方法弥补了医疗沟通中的关键空白，使AI系统建立在文化有效表征基础上，推动多语言环境下更包容、以患者为中心的心理健康自然语言处理工具发展。
原文摘要 · Abstract (English)
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.
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