关键覆盖至关重要：半结构化提取OCR临床报告
Key Coverage Matters: Semi-Structured Extraction of OCR Clinical Reports
AI总结：
本文提出通过半结构化方法提取OCR临床报告中的关键信息，通过迭代挖掘和验证关键字段，提升提取性能，实验显示关键覆盖度对整体性能有显著影响。
摘要：
临床报告常因隐私法规和数据孤岛分散在不同医疗机构，患者在不同医院就诊时通常携带纸质或扫描报告，这阻碍了电子健康记录（EHR）的整合和长期审查，影响患者管理、随访护理、真实世界研究和临床试验匹配。尽管OCR可以数字化这些报告，但可靠提取仍具挑战，因临床文档异构、OCR文本噪声及许多医疗环境需要低成本本地部署。我们将其问题建模为基于OCR衍生临床报告的典型关键-条件抽取问答任务。由于关键字段在事前既不固定也不已知，关键空间是开放的。通过迭代关键挖掘、规范化、聚类和轻量级人工验证维护典型关键库存，并引入关键覆盖作为衡量库存完整性的指标。使用0.2B BERT模型，在超过20家医院的真实报告上进行实验，实验结果显示性能随关键覆盖度单调提高。当覆盖前90个典型关键时，模型在精确匹配和边界容忍匹配下的F1分数分别为0.839和0.893。这些结果表明关键覆盖是端到端性能的主导因素。在前90个关键覆盖时，模型在精确匹配下优于微调后的Qwen3-0.6B基线。尽管我们的注释语料是中文的，但该方法依赖于半结构化临床报告的语言无关键值组织，只要具备适当的典型关键库存和别名映射，即可适应其他环境。
英文摘要：
Clinical reports are often fragmented across healthcare institutions because privacy regulations and data silos limit direct information sharing. When patients seek care at a different hospital, they often carry paper or scanned reports from prior visits. This hinders EHR integration and longitudinal review, and downstream applications that depend on more complete patient records, such as patient management, follow-up care, real-world studies, and clinical-trial matching. Although OCR can digitize such reports, reliable extraction remains challenging because clinical documents are heterogeneous, OCR text is noisy, and many healthcare settings require low-cost on-premise deployment. We formulate this problem as canonical key-conditioned extractive question answering over OCR-derived clinical reports. Because the key fields are neither fixed nor known in advance, the key space is open. We maintain a canonical key inventory through iterative key mining, normalization, clustering, and lightweight human verification, and introduce key coverage as a metric to quantify inventory completeness. Using a 0.2B BERT-based model, experiments on real-world reports from more than 20 hospitals show performance improves monotonically with key coverage. The model achieves F1 scores of 0.839 and 0.893 under exact match and boundary-tolerant matching, respectively, once the Top-90 canonical keys are covered. These results show that key coverage is a dominant factor for end-to-end performance. At Top-90 coverage, our model outperforms a fine-tuned Qwen3-0.6B baseline under exact match. Although our annotated corpus is Chinese, the method relies on the language-agnostic key-value organization of semi-structured clinical reports and can be adapted to other settings given an appropriate canonical key inventory and alias mapping.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
