基于生成式LLM驱动的个性化风险评估对话式AI应用：以COVID-19为例
Generative LLM Powered Conversational AI Application for Personalized Risk Assessment: A Case Study in COVID-19
AI总结：
本研究提出基于生成式LLM的流式对话风险评估方法，通过微调Llama2-7b和Flan-t5-xl构建移动应用，实现无代码COVID-19严重程度评估，在低数据场景下表现优于传统分类器并提供可解释性。
摘要：
大型语言模型（LLMs）在多种自然语言任务中展现出卓越能力，正日益被应用于医疗保健领域。本研究展示了一种基于LLM的新型疾病风险评估方法，通过流式人机对话实现，消除了传统机器学习方法所需的编程需求。在一项COVID-19严重程度风险评估案例研究中，我们使用少量自然语言样本对预训练的生成式LLMs（如Llama2-7b和Flan-t5-xl）进行微调，并将其性能与通过表格数据从头训练的传统分类器（即Logistic Regression、XGBoost、Random Forest）在不同实验设置下的表现进行比较。我们开发了一款移动应用程序，以这些微调后的LLMs作为其生成式AI（GenAI）核心，促进临床医生与患者之间的实时交互，通过对话界面提供无代码风险评估。这种集成不仅允许使用流式问答（QA）作为输入，还提供从LLM注意力层导出的个性化特征重要性分析，从而增强风险评估的可解释性。通过在有限微调样本下实现高AUC分数，我们的结果证明生成式LLMs在低数据场景下具有超越判别式分类方法的潜力，突显了其在现实世界中的适应性和有效性。本研究旨在填补利用生成式LLMs进行交互式无代码风险评估的现有空白，并鼓励这一新兴领域的进一步研究。
英文摘要：
Large language models (LLMs) have shown remarkable capabilities in various natural language tasks and are increasingly being applied in healthcare domains. This work demonstrates a new LLM-powered disease risk assessment approach via streaming human-AI conversation, eliminating the need for programming required by traditional machine learning approaches. In a COVID-19 severity risk assessment case study, we fine-tune pre-trained generative LLMs (e.g., Llama2-7b and Flan-t5-xl) using a few shots of natural language examples, comparing their performance with traditional classifiers (i.e., Logistic Regression, XGBoost, Random Forest) that are trained de novo using tabular data across various experimental settings. We develop a mobile application that uses these fine-tuned LLMs as its generative AI (GenAI) core to facilitate real-time interaction between clinicians and patients, providing no-code risk assessment through conversational interfaces. This integration not only allows for the use of streaming Questions and Answers (QA) as inputs but also offers personalized feature importance analysis derived from the LLM's attention layers, enhancing the interpretability of risk assessments. By achieving high Area Under the Curve (AUC) scores with a limited number of fine-tuning samples, our results demonstrate the potential of generative LLMs to outperform discriminative classification methods in low-data regimes, highlighting their real-world adaptability and effectiveness. This work aims to fill the existing gap in leveraging generative LLMs for interactive no-code risk assessment and to encourage further research in this emerging field.
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。
