应用大语言模型进行非小细胞肺癌因果结构学习
Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer
AI总结：
本研究探索利用大语言模型预测非小细胞肺癌因果图中边的方向性，在去标识化患者数据上验证，结果表明其性能优于现有最先进方法，助力医学因果发现。
摘要：
因果发现正成为医学人工智能研究中的关键组成部分。这些方法通过识别生物标志物、人口统计学特征、治疗手段与结局之间的因果联系，能够增强医疗保健水平，并帮助医疗专业人员选择更具影响力的治疗方案和策略。与此同时，大语言模型（LLMs）在从文本数据中识别模式和生成见解方面展现出巨大潜力。在本文中，我们研究将大语言模型应用于确定因果发现中边的方向性问题。具体而言，我们在一个去标识化的非小细胞肺癌（NSCLC）患者数据集上测试我们的方法，该数据集同时包含电子健康记录和基因组面板数据。使用基于表格数据的贝叶斯狄利克雷估计器对图进行验证。我们的结果表明，大语言模型能够准确预测因果图中边的方向性，优于现有的最先进方法。这些发现表明，大语言模型可以在推进因果发现方面发挥重要作用，并帮助我们更好地理解复杂系统。
英文摘要：
Causal discovery is becoming a key part in medical AI research. These methods can enhance healthcare by identifying causal links between biomarkers, demographics, treatments and outcomes. They can aid medical professionals in choosing more impactful treatments and strategies. In parallel, Large Language Models (LLMs) have shown great potential in identifying patterns and generating insights from text data. In this paper we investigate applying LLMs to the problem of determining the directionality of edges in causal discovery. Specifically, we test our approach on a deidentified set of Non Small Cell Lung Cancer(NSCLC) patients that have both electronic health record and genomic panel data. Graphs are validated using Bayesian Dirichlet estimators using tabular data. Our result shows that LLMs can accurately predict the directionality of edges in causal graphs, outperforming existing state-of-the-art methods. These findings suggests that LLMs can play a significant role in advancing causal discovery and help us better understand complex systems.
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。
