Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Response
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918155456086016 |
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| author | Shi, Daqian Diao, Xiaolei Wu, Jinge Wu, Honghan Tang, Xiongfeng Naughton, Felix Bondaronek, Paulina |
| author_facet | Shi, Daqian Diao, Xiaolei Wu, Jinge Wu, Honghan Tang, Xiongfeng Naughton, Felix Bondaronek, Paulina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05196 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Response Shi, Daqian Diao, Xiaolei Wu, Jinge Wu, Honghan Tang, Xiongfeng Naughton, Felix Bondaronek, Paulina Artificial Intelligence 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. |
| title | Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Response |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.05196 |