Shuang Yang, Xi Yang, Tianchen Lyu, Xing He, Dejana Braithwaite, Hiren J Mehta, Yi Guo, Yonghui Wu, Jiang Bian
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引用次数: 1
摘要
本研究旨在开发一种自然语言处理（NLP）工具，从自由文本临床叙述中提取肺结节和结节特征信息。我们确定了佛罗里达大学医疗系统中接受低剂量计算机断层扫描（LDCT）的 3080 名患者，并收集了他们的临床叙述，包括电子健康记录（EHR）中的放射学报告。然后，我们人工标注了 394 份报告作为黄金标准语料库，并探索了三种最先进的基于转换器的 NLP 方法。最佳模型的 F1 分数为 0.9279。
A Preliminary Study of Extracting Pulmonary Nodules and Nodule Characteristics from Radiology Reports Using Natural Language Processing.
This study aims to develop a natural language processing (NLP) tool to extract the pulmonary nodules and nodule characteristics information from free-text clinical narratives. We identified a cohort of 3,080 patients who received low dose computed tomography (LDCT) at the University of Florida health system and collected their clinical narratives including radiology reports in their electronic health records (EHRs). Then, we manually annotated 394 reports as the gold-standard corpus and explored three state-of-the-art transformer-based NLP methods. The best model achieved an F1-score of 0.9279.
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