用多层大模型框架提升阿拉伯语医疗文本疾病预测准确率
A Multi-Layered Large Language Model Framework for Disease Prediction
- 融合文本摘要、优化与命名实体识别的三层预处理流程
- 采用CAMeL-BERT+NER增强文本，疾病类型分类率达83%
- 适合关注低资源语言医疗AI的开发者与研究者
社交远程医疗通过让用户在社交媒体和在线健康平台分享症状，彻底改变了医疗模式，形成了海量可利用的医疗数据。大型语言模型（如LLAMA3、GPT-3.5 Turbo、BERT）能处理复杂医学文本，提升疾病分类能力。本研究探索了三种阿拉伯语医学文本预处理方法：文本摘要、文本优化和命名实体识别（NER）。对比评估了CAMeL-BERT、AraBERT和Asafaya-BERT配合LoRA微调的效果，结果显示使用经过NER增强的文本时，CAMeL-BERT表现最佳，疾病类型分类准确率为83%，严重程度评估达69%。未微调模型表现较差（类型分类13%-20%，严重程度40%-49%）。将大模型融入社交远程医疗系统可显著提升诊断准确性和治疗效果。
原文摘要 · Abstract (English)
Social telehealth has revolutionized healthcare by enabling patients to share symptoms and receive medical consultations remotely. Users frequently post symptoms on social media and online health platforms, generating a vast repository of medical data that can be leveraged for disease classification and symptom severity assessment. Large language models (LLMs), such as LLAMA3, GPT-3.5 Turbo, and BERT, process complex medical data to enhance disease classification. This study explores three Arabic medical text preprocessing techniques: text summarization, text refinement, and Named Entity Recognition (NER). Evaluating CAMeL-BERT, AraBERT, and Asafaya-BERT with LoRA, the best performance was achieved using CAMeL-BERT with NER-augmented text (83% type classification, 69% severity assessment). Non-fine-tuned models performed poorly (13%-20% type classification, 40%-49% severity assessment). Integrating LLMs into social telehealth systems enhances diagnostic accuracy and treatment outcomes.
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