SwasthLLM：基于对比表示的统一跨语言、多任务与元学习零样本医学诊断框架
SwasthLLM: a Unified Cross-Lingual, Multi-Task, and Meta-Learning Zero-Shot Framework for Medical Diagnosis Using Contrastive Representations
AI总结：
SwasthLLM提出统一跨语言多任务元学习零样本框架，利用对比表示实现英语、印地语和孟加拉语医学诊断，零样本准确率分别达92.78%和73.33%。
摘要：
在多语言医疗环境中，由于低资源语言中医学标注数据的稀缺以及不同人群间的语言变异性，从临床文本进行自动疾病诊断仍然是一项具有挑战性的任务。本文提出了SwasthLLM，一个统一的、零样本的、跨语言和多任务学习的医学诊断框架，能够在英语、印地语和孟加拉语中有效运行，而无需针对特定语言进行微调。其核心是，SwasthLLM利用多语言的XLM-RoBERTa编码器，并增强了一个语言感知注意力机制和一个疾病分类头，使模型能够提取医学相关信息，无论语言结构如何。为了跨语言对齐语义表示，引入了一个孪生对比学习模块，确保不同语言中等价的医学文本产生相似的嵌入。此外，一个翻译一致性模块和一个对比投影头强化了语言不变表示学习。SwasthLLM采用多任务学习策略进行训练，联合优化疾病分类、翻译对齐和对比学习目标。此外，我们采用模型无关元学习（MAML）使模型具备在数据极少的情况下对未见语言或任务快速适应的能力。我们的分阶段训练流程强调在任务特定微调之前进行稳健的表示对齐。广泛评估表明，SwasthLLM实现了高诊断性能，在监督设置下测试准确率为97.22%，F1分数为97.17%。关键的是，在零样本场景中，它在印地语上达到92.78%的准确率，在孟加拉语医学文本上达到73.33%的准确率，展示了在低资源环境中的强大泛化能力。
英文摘要：
In multilingual healthcare environments, automatic disease diagnosis from clinical text remains a challenging task due to the scarcity of annotated medical data in low-resource languages and the linguistic variability across populations. This paper proposes SwasthLLM, a unified, zero-shot, cross-lingual, and multi-task learning framework for medical diagnosis that operates effectively across English, Hindi, and Bengali without requiring language-specific fine-tuning. At its core, SwasthLLM leverages the multilingual XLM-RoBERTa encoder augmented with a language-aware attention mechanism and a disease classification head, enabling the model to extract medically relevant information regardless of the language structure. To align semantic representations across languages, a Siamese contrastive learning module is introduced, ensuring that equivalent medical texts in different languages produce similar embeddings. Further, a translation consistency module and a contrastive projection head reinforce language-invariant representation learning. SwasthLLM is trained using a multi-task learning strategy, jointly optimizing disease classification, translation alignment, and contrastive learning objectives. Additionally, we employ Model-Agnostic Meta-Learning (MAML) to equip the model with rapid adaptation capabilities for unseen languages or tasks with minimal data. Our phased training pipeline emphasizes robust representation alignment before task-specific fine-tuning. Extensive evaluation shows that SwasthLLM achieves high diagnostic performance, with a test accuracy of 97.22% and an F1-score of 97.17% in supervised settings. Crucially, in zero-shot scenarios, it attains 92.78% accuracy on Hindi and 73.33% accuracy on Bengali medical text, demonstrating strong generalization in low-resource 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。
