用图模型同时纠正数据漏报和错报，提升医疗公共安全数据可靠性。
Graph-Based Prediction Models for Data Debiasing
- 构建地理或特征相似图，将报告偏差视为图上平滑信号
- 在真实数据集上恢复出更接近真实事件数的修正结果
- 适合处理有报告偏差的公共卫生与应急响应数据
数据采集中的漏报和错报问题在医疗和公共安全等关键应用中带来严峻挑战。本文提出图基漏报与错报去偏框架GROUD，通过联合估计真实事件数量与报告偏差概率来校正数据。基于地理或特征相似性构建图结构，将偏差建模为图上的平滑信号，采用凸优化方法，确保解的唯一性，并在特定假设下具备理论恢复保证。我们在模拟实验及真实数据集（包括亚特兰大紧急呼叫数据、新冠疫苗不良反应报告）上验证了GROUD的鲁棒性与优越性能，能更准确地恢复去偏后的事件计数。该方法为受报告不实影响的系统提供了更可靠的下游决策支持。
原文摘要 · Abstract (English)
Bias in data collection, arising from both under-reporting and over-reporting, poses significant challenges in critical applications such as healthcare and public safety. In this work, we introduce Graph-based Over- and Under-reporting Debiasing (GROUD), a novel graph-based optimization framework that debiases reported data by jointly estimating the true incident counts and the associated reporting bias probabilities. By modeling the bias as a smooth signal over a graph constructed from geophysical or feature-based similarities, our convex formulation not only ensures a unique solution but also comes with theoretical recovery guarantees under certain assumptions. We validate GROUD on both challenging simulated experiments and real-world datasets -- including Atlanta emergency calls and COVID-19 vaccine adverse event reports -- demonstrating its robustness and superior performance in accurately recovering debiased counts. This approach paves the way for more reliable downstream decision-making in systems affected by reporting irregularities.
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