Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Response

• URL: http://arxiv.org/abs/2510.05196v1
• Date: Mon, 06 Oct 2025 16:10:18 GMT
• Title: Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Response
• Authors: Daqian Shi, Xiaolei Diao, Jinge Wu, Honghan Wu, Xiongfeng Tang, Felix Naughton, Paulina Bondaronek,
• Abstract summary: Timely and accurate analysis of population-level data is crucial for effective decision-making during public health emergencies such as the COVID-19 pandemic.<n>Massive input of semi-structured data, including structured demographic information and unstructured human feedback, poses significant challenges to conventional analysis methods.<n>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.
• Score: 13.410706647320088
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• DANIEL: A Distributed and Scalable Approach for Global Representation Learning with EHR Applications [8.530466871734564]
  probabilistic clustering models face fundamental challenges in modern data environments.<n>We develop a distributed framework that enables scalable and privacy representation learning from binary data.<n>We evaluate our algorithm on multi-institutional electronic health record (EHR) datasets.
  arXiv  Detail & Related papers  (2025-11-04T17:35:12Z)
• Adaptation of Foundation Models for Medical Image Analysis: Strategies, Challenges, and Future Directions [4.332241609032423]
  Foundation models (FMs) have emerged as a transformative paradigm in medical image analysis.<n>This review presents a comprehensive assessment of strategies for adapting FMs to the specific demands of medical imaging.
  arXiv  Detail & Related papers  (2025-11-03T06:57:42Z)
• Integrating Genomics into Multimodal EHR Foundation Models [56.31910745104141]
  This paper introduces an innovative EHR foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality.<n>The framework aims to learn complex relationships between clinical data and genetic predispositions.<n>This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies.
  arXiv  Detail & Related papers  (2025-10-24T15:56:40Z)
• Evaluating LLMs for Demographic-Targeted Social Bias Detection: A Comprehensive Benchmark Study [1.6682715542079583]
  Large-scale web-scraped text corpora used to train general-purpose AI models often contain harmful demographic-targeted social biases.<n>We present a comprehensive evaluation framework aimed at English texts to assess the ability of LLMs in detecting demographic-targeted social biases.<n>We then conduct a systematic evaluation with models across scales and techniques, including prompting, in-context learning, and fine-tuning.
  arXiv  Detail & Related papers  (2025-10-06T09:45:32Z)
• General Demographic Foundation Models for Enhancing Predictive Performance Across Diseases [0.39508022083907385]
  This study proposes a General Demographic Pre-trained (GDP) model as a foundational representation framework tailored to age and gender.<n>The model is pre-trained and evaluated using datasets with diverse diseases and population compositions from different geographic regions.
  arXiv  Detail & Related papers  (2025-09-09T02:02:27Z)
• Scaling Multi-Agent Epistemic Planning through GNN-Derived Heuristics [1.2103159258004725]
  Multi-agent Epistemic Planning (MEP) is an autonomous planning framework for reasoning about both the physical world and the beliefs of agents.<n>The MEP requires states to be represented as Kripke structures, i.e., directed labeled graphs.<n>We exploit Graph Neural Networks (GNNs) to learn patterns and relational structures within epistemic states, to guide the planning process.
  arXiv  Detail & Related papers  (2025-08-18T11:26:20Z)
• Robust Molecular Property Prediction via Densifying Scarce Labeled Data [53.24886143129006]
  In drug discovery, compounds most critical for advancing research often lie beyond the training set.<n>We propose a novel bilevel optimization approach that leverages unlabeled data to interpolate between in-distribution (ID) and out-of-distribution (OOD) data.
  arXiv  Detail & Related papers  (2025-06-13T15:27:40Z)
• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics [8.692647930497936]
  We use conformal analysis to quantify the predictive uncertainty of a vision transformer based foundation model.<n>We show how this can be used as a fairness metric to evaluate the robustness of the feature embeddings of the foundation model.
  arXiv  Detail & Related papers  (2025-03-31T08:06:00Z)
• LLM-based Agent Simulation for Maternal Health Interventions: Uncertainty Estimation and Decision-focused Evaluation [30.334268991701727]
  Agent-based simulation is crucial for modeling complex human behavior.<n>Traditional approaches require extensive domain knowledge and large datasets.<n>Large language models (LLMs) offer a promising alternative by leveraging broad world knowledge.
  arXiv  Detail & Related papers  (2025-03-25T20:24:47Z)
• Stateful Offline Contextual Policy Evaluation and Learning [88.9134799076718]
  We study off-policy evaluation and learning from sequential data. We formalize the relevant causal structure of problems such as dynamic personalized pricing. We show improved out-of-sample policy performance in this class of relevant problems.
  arXiv  Detail & Related papers  (2021-10-19T16:15:56Z)
• SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models [48.07469930813923]
  This work aims to forecast the demand for healthcare services, by predicting the number of patient visits to healthcare facilities. We introduce SANSformer, an attention-free sequential model designed with specific inductive biases to cater for the unique characteristics of EHR data. Our results illuminate the promising potential of tailored attention-free models and self-supervised pretraining in refining healthcare utilization predictions across various patient demographics.
  arXiv  Detail & Related papers  (2021-08-31T08:23:56Z)
• Health Status Prediction with Local-Global Heterogeneous Behavior Graph [69.99431339130105]
  Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors. We propose to model the behavior-related multi-source data streams with a local-global graph. We take experiments on StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.
  arXiv  Detail & Related papers  (2021-03-23T11:10:04Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.