面向印度语言的跨语言心理健康本体：通过可解释人工智能与人类在环验证连接患者表达与临床理解
Cross-Lingual Mental Health Ontologies for Indian Languages: Bridging Patient Expression and Clinical Understanding through Explainable AI and Human-in-the-Loop Validation
AI总结：
针对印度语言心理健康沟通的语言碎片化与临床NLP资源缺口问题，提出患者压力表达跨语言图（CL-PDE）框架，通过图方法构建跨语言本体以捕捉文化嵌入的痛苦表达并关联临床术语，助力多语言心理健康护理。
摘要：
印度的心理健康沟通存在语言碎片化、文化多样性的特点，且在临床自然语言处理（NLP）中常被低估。当前健康本体和心理健康资源以英语或西方文化为中心的诊断框架为主，在印度语言的患者痛苦表达表征方面存在缺口。我们提出患者压力表达跨语言图（CL-PDE），这是一个通过基于图的方法构建跨语言心理健康本体的框架，可捕捉文化嵌入的痛苦表达、跨语言对齐并与临床术语关联。该方法通过将人工智能系统建立在文化有效的表征基础上，解决医疗沟通中的关键缺口，为多语言环境下的心理健康护理提供更具包容性和以患者为中心的NLP工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
