构建医疗险理赔判例语料库，助力患者理解保险条款
Health Insurance Coverage Rule Interpretation Corpus: Law, Policy, and Medical Guidance for Health Insurance Coverage Understanding
- 收集真实法律与医学文本，构建美国医保语料库
- 提出理赔结果预测任务，准确率超基线模型
- 适合医保政策研究者与患者自助维权使用
美国医保体系复杂，理解不足与司法资源匮乏对弱势群体影响严重。自然语言处理技术为实现精准、个案化的理解提供了可能，有助于提升司法可及性与医疗保障。然而现有语料库缺乏评估简单案件所需的上下文信息。本文收集并发布了一套权威的法律与医学文本语料，涵盖美国医保相关内容；提出一个针对医保申诉的结果预测任务，用于支持监管决策与患者自助；同时发布了标注基准数据集及在该数据集上训练的模型。
原文摘要 · Abstract (English)
U.S. health insurance is complex, and inadequate understanding and limited access to justice have dire implications for the most vulnerable. Advances in natural language processing present an opportunity to support efficient, case-specific understanding, and to improve access to justice and healthcare. Yet existing corpora lack context necessary for assessing even simple cases. We collect and release a corpus of reputable legal and medical text related to U.S. health insurance. We also introduce an outcome prediction task for health insurance appeals designed to support regulatory and patient self-help applications, and release a labeled benchmark for our task, and models trained on it.
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