背景:病理学报告可作为患者临床叙述的可审计试验,包含与诊断有关的文本,预后,和标本处理。最近的工作已经利用自然语言处理(NLP)管道,其中包括基于规则或机器学习的分析,以揭示告知临床终点和生物标志物信息的文本模式。尽管深度学习方法已经走到了NLP的最前沿,在提取用于预测医疗程序信息的关键见解方面,与其他机器学习方法的性能进行了有限的比较,用于告知病理科的报销。此外,与仅使用诊断字段来预测当前程序术语(CPT)代码和签署病理学家相比,将来自多个报告子字段的信息进行合并和排名的效用尚不清楚.
方法:预处理病理报告后,我们利用高级主题建模来确定达特茅斯-希区柯克病理学和实验室医学系(DPLM)93,039例病理报告队列的特征主题.我们分别比较了XGBoost,SVM,和BERT(来自变压器的双向编码器表示)方法,用于预测主要CPT代码(CPT88302、88304、88305、88307、88309)以及38个辅助CPT代码,使用单独的诊断文本和所有子字段的文本。我们进行了类似的分析,以表征来自20名病理学家中最多的病理报告签出的文本。最后,我们通过使用模型解释技术发现了重要的报告子组件。
结果:我们确定了20个与诊断和程序信息相关的主题。仅对诊断文本进行操作,BERT在预测主CPT代码方面优于XGBoost。使用所有报告子字段时,XGBoost在预测主CPT代码方面优于BERT。利用病理报告的其他子字段提高了辅助CPT代码的预测准确性,对于主要CPT代码的XGBoost模型,使用其他报告子字段的性能增益很高。CPT代码的错误分类在相似复杂度的代码之间,病理学家之间的错误分类与亚专科有关。
结论:我们的方法产生的CPT代码预测的准确性高于先前报道的。尽管诊断文本是重要的信息来源,可以从其他报告子字段中提取其他见解。尽管BERT方法的性能与XGBoost方法相当,它们可能会将有价值的信息提供给组合图像的管道,文本,和-组学信息。存在未来的资源节约机会,以帮助医院检测错误的计费,规范报告文本,并估计与病理学家补偿(RVU)相关的生产率指标。
BACKGROUND: Pathology
reports serve as an auditable trial of a patient\'s clinical narrative, containing text pertaining to diagnosis, prognosis, and specimen processing. Recent works have utilized natural language processing (NLP) pipelines, which include rule-based or machine-learning analytics, to uncover textual patterns that inform clinical endpoints and biomarker information. Although deep learning methods have come to the forefront of NLP, there have been limited comparisons with the performance of other machine-learning methods in extracting key insights for the prediction of medical procedure information, which is used to inform reimbursement for pathology departments. In addition, the utility of combining and ranking information from multiple report subfields as compared with exclusively using the diagnostic field for the prediction of Current Procedural Terminology (CPT) codes and signing pathologists remains unclear.
METHODS: After preprocessing pathology
reports, we utilized advanced topic modeling to identify topics that characterize a cohort of 93,039 pathology
reports at the Dartmouth-Hitchcock Department of Pathology and Laboratory Medicine (DPLM). We separately compared XGBoost, SVM, and BERT (Bidirectional Encoder Representation from Transformers) methodologies for the prediction of primary CPT codes (CPT 88302, 88304, 88305, 88307, 88309) as well as 38 ancillary CPT codes, using both the diagnostic text alone and text from all subfields. We performed similar analyses for characterizing text from a group of the 20 pathologists with the most pathology report sign-outs. Finally, we uncovered important report subcomponents by using model explanation techniques.
RESULTS: We identified 20 topics that pertained to diagnostic and procedural information. Operating on diagnostic text alone, BERT outperformed XGBoost for the prediction of primary CPT codes. When utilizing all report subfields, XGBoost outperformed BERT for the prediction of primary CPT codes. Utilizing additional subfields of the pathology report increased prediction accuracy across ancillary CPT codes, and performance gains for using additional report subfields were high for the XGBoost model for primary CPT codes. Misclassifications of CPT codes were between codes of a similar complexity, and misclassifications between pathologists were subspecialty related.
CONCLUSIONS: Our approach generated CPT code predictions with an accuracy that was higher than previously reported. Although diagnostic text is an important source of information, additional insights may be extracted from other report subfields. Although BERT approaches performed comparably to the XGBoost approaches, they may lend valuable information to pipelines that combine image, text, and -omics information. Future resource-saving opportunities exist to help hospitals detect mis-billing, standardize report text, and estimate productivity metrics that pertain to pathologist compensation (RVUs).