用图模型融合人口数据与公众反馈，动态生成政策建议。
Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Response
- 构建需知感知图，融合年龄、性别等人口特征与公众意见
- 弱监督下实现跨领域分析，支持动态政策响应
- 适合公共卫生部门在资源有限时做智能决策支持
在新冠疫情等公共卫生危机中，及时准确的人口数据分析对决策至关重要。然而，半结构化数据（如人口统计信息与非结构化公众反馈）的海量输入给传统分析方法带来挑战。人工专家评估虽准确但效率低，标准NLP流程需大量标注数据且泛化能力差。为此，我们提出一种基于图的推理框架，将大语言模型与人口属性及公众反馈结合，在弱监督下动态建模市民需求，构建需知感知图，基于年龄、性别、多重剥夺指数等关键特征进行个性化分析，生成可解释洞察以支持响应式健康政策制定。我们在真实数据集上测试，初步实验结果验证了其可行性。该方法为资源受限的临床与政府机构提供可扩展的人群健康智能监测方案。
原文摘要 · Abstract (English)
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.
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