用于医疗文本生成的阿拉伯语大语言模型
Arabic Large Language Models for Medical Text Generation
AI总结：
本研究提出微调大语言模型以生成阿拉伯语医疗文本，通过收集并处理社交媒体上的医患对话数据集，微调了Mistral-7B等模型，结果显示微调后的Mistral-7B在各项评估指标上表现最优，验证了生成式AI在推进医院管理系统方面的潜力。
摘要：
高效的医院管理系统（HMS）在全球范围内对于应对拥挤、资源有限和紧急医疗服务可用性差等挑战至关重要。现有方法通常缺乏提供准确、实时医疗建议的能力，特别是在处理不规则输入和代表性不足的语言时。为了克服这些局限性，本研究提出了一种针对阿拉伯语医疗文本生成微调大语言模型（LLMs）的方法。该系统旨在根据用户输入，通过提供准确的医疗建议、诊断、药物推荐和治疗方案来辅助患者。研究方法需要从社交媒体平台收集独特的数据集，捕捉患者与医生之间的真实医疗对话。该数据集包含患者投诉和医疗建议，经过适当的清理和预处理以适应多种阿拉伯语方言。微调最先进的生成模型，如Mistral-7B-Instruct-v0.2、LLaMA-2-7B和GPT-2 Medium，优化了系统生成可靠医疗文本的能力。评估结果表明，微调后的Mistral-7B模型优于其他模型，在精确率、召回率和F1分数上分别达到了68.5%、69.08%和68.5%的平均BERT（来自Transformers的双向编码器表示）分数。比较基准测试和定性评估验证了系统对非正式输入产生连贯且相关医疗回复的能力。本研究突出了生成式人工智能（AI）在推进HMS方面的潜力，为全球医疗保健挑战提供了可扩展且适应性强的解决方案，特别是在语言和文化多样化的环境中。
英文摘要：
Efficient hospital management systems (HMS) are critical worldwide to address challenges such as overcrowding, limited resources, and poor availability of urgent health care. Existing methods often lack the ability to provide accurate, real-time medical advice, particularly for irregular inputs and underrepresented languages. To overcome these limitations, this study proposes an approach that fine-tunes large language models (LLMs) for Arabic medical text generation. The system is designed to assist patients by providing accurate medical advice, diagnoses, drug recommendations, and treatment plans based on user input. The research methodology required the collection of a unique dataset from social media platforms, capturing real-world medical conversations between patients and doctors. The dataset, which includes patient complaints together with medical advice, was properly cleaned and preprocessed to account for multiple Arabic dialects. Fine-tuning state-of-the-art generative models, such as Mistral-7B-Instruct-v0.2, LLaMA-2-7B, and GPT-2 Medium, optimized the system's ability to generate reliable medical text. Results from evaluations indicate that the fine-tuned Mistral-7B model outperformed the other models, achieving average BERT (Bidirectional Encoder Representations from Transformers) Score values in precision, recall, and F1-scores of 68.5\%, 69.08\%, and 68.5\%, respectively. Comparative benchmarking and qualitative assessments validate the system's ability to produce coherent and relevant medical replies to informal input. This study highlights the potential of generative artificial intelligence (AI) in advancing HMS, offering a scalable and adaptable solution for global healthcare challenges, especially in linguistically and culturally diverse environments.
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。
