Abstract
Timely and accurate analysis of population-level data is crucial for effective decision-making during public health emergencies such as the COVID-19 pandemic. However, the massive input of semi-structured data, including structured demographic information and unstructured human feedback, poses significant challenges to conventional analysis methods. Manual expert-driven assessments, though accurate, are inefficient, while standard NLP pipelines often require large task-specific labeled datasets and struggle with generalization across diverse domains. To address these challenges, we propose a novel graph-based reasoning framework that integrates large language models with structured demographic attributes and unstructured public feedback in a weakly supervised pipeline. The proposed approach dynamically models evolving citizen needs into a need-aware graph, enabling population-specific analyses based on key features such as age, gender, and the Index of Multiple Deprivation. It generates interpretable insights to inform responsive health policy decision-making. We test our method using a real-world dataset, and preliminary experimental results demonstrate its feasibility. This approach offers a scalable solution for intelligent population health monitoring in resource-constrained clinical and governmental settings.
Abstract (translated)
及时且准确地分析人口层面的数据对于在如新冠疫情等公共卫生紧急情况下的有效决策至关重要。然而，包括结构化的人口统计信息和非结构化的公众反馈在内的大量半结构化数据输入对传统分析方法构成了重大挑战。虽然专家驱动的手动评估尽管精确但效率低下，而标准的自然语言处理（NLP）流程通常需要大量的特定任务标签数据，并且在跨多种领域时难以实现泛化。为了解决这些问题，我们提出了一种基于图推理的新框架，该框架将大型语言模型与结构化的个人属性和非结构化的公共反馈相结合，在一个弱监督管道中运行。所提出的这种方法能够动态地将不断变化的公民需求建模成一个需要意识的图形，允许根据年龄、性别以及多种剥夺指数等关键特征进行特定人口群体的分析，并生成可解释的见解以支持响应性的卫生政策决策制定。 我们使用真实世界的数据集测试了该方法，并且初步实验结果表明其可行性。这一方法为资源有限的临床和政府环境中的人口健康智能监控提供了一个可扩展的解决方案。
URL
https://arxiv.org/abs/2510.05196
