目的:垂体腺瘤(PAs)的诊断报告是复杂且未标准化的。我们旨在确定陈述中最常用的元素及其在现实世界临床实践中的组合模式和变化,以促进标准化诊断记录和建立高效的元素提取流程为最终目标。
方法:纳入了2012年至2020年的患者病历,其中包括前三个诊断中的PA。在诊断文本中手动标记元素后,我们获得了元素类型和训练集,据此,基于分词模型“Jieba”构建了信息提取模型,以提取其余诊断文本中包含的信息。
结果:从3770份医疗记录的4010份文本中,总共576份不同的诊断陈述被纳入分析。与PA相关的前十个诊断要素是组织病理学,肿瘤位置,内分泌状态,肿瘤大小,侵入性,复发,诊断确认,Knosp等级,残余肿瘤,和折射。自动提取模型在第二轮中实现了所有十个元素的F1分数达到100%,在由532个额外的诊断文本组成的测试集中达到了97.3-100.0%。肿瘤位置,内分泌状态,组织病理学,肿瘤大小是最常用的因素,由上述要素组成的诊断是最常见的。内分泌状态具有最大的表达变异性,其次是Knosp等级。在所有的术语中,肿瘤大小丢失的百分比最高(21%).在主要诊断为PA的陈述中,18.6%没有肿瘤大小的信息,而对于那些有其他诊断的人来说,这一百分比上升到48%(P<0.001)。
结论:在现实世界的临床实践中,PA诊断声明的标准化并不令人满意。这项研究可以帮助标准化PA诊断的结构化模式,并为研究友好性奠定基础,高质量的临床信息提取。
Diagnostic statements for pituitary adenomas (PAs) are complex and unstandardized. We aimed to determine the most commonly used elements contained in the statements and their combination patterns and variations in real-world clinical practice, with the ultimate goal of promoting standardized diagnostic recording and establishing an efficient element extraction process.
Patient medical records from 2012 to 2020 that included PA among the first three diagnoses were included. After manually labeling the elements in the diagnostic texts, we obtained element types and training sets, according to which an information extraction model was constructed based on the word segmentation model \"Jieba\" to extract information contained in the remaining diagnostic texts.
A total of 576 different diagnostic statements from 4010 texts of 3770 medical records were enrolled in the analysis. The first ten diagnostic elements related to PA were histopathology, tumor location, endocrine status, tumor size, invasiveness, recurrence, diagnostic confirmation, Knosp grade, residual tumor, and refractoriness. The automated extraction model achieved F1-scores that reached 100% for all ten elements in the second round and 97.3-100.0% in the test set consisting of an additional 532 diagnostic texts. Tumor location, endocrine status, histopathology, and tumor size were the most commonly used elements, and diagnoses composed of the above elements were the most frequent. Endocrine status had the greatest expression variability, followed by Knosp grade. Among all the terms, the percentage of loss of tumor size was among the highest (21%). Among statements where the principal diagnoses were PAs, 18.6% did not have information on tumor size, while for those with other diagnoses, this percentage rose to 48% (P < 0.001).
Standardization of the diagnostic statement for PAs is unsatisfactory in real-world clinical practice. This study could help standardize a structured pattern for PA diagnosis and establish a foundation for research-friendly, high-quality clinical information extraction.