Ganguly Indrila, Buhrman Graham, Kline Ed, Mun Seong K, Sengupta Srijan
Department of Statistics, North Carolina State University, Raleigh, NC 27607, USA.
Department of Educational Psychology, University of Wisconsin-Madison, Madison, WI 53706, USA.
Diagnostics (Basel). 2023 Mar 23;13(7):1215. doi: 10.3390/diagnostics13071215.
A report published in 2000 from the Institute of Medicine revealed that medical errors were a leading cause of patient deaths, and urged the development of error detection and reporting systems. The field of radiation oncology is particularly vulnerable to these errors due to its highly complex process workflow, the large number of interactions among various systems, devices, and medical personnel, as well as the extensive preparation and treatment delivery steps. Natural language processing (NLP)-aided statistical algorithms have the potential to significantly improve the discovery and reporting of these medical errors by relieving human reporters of the burden of event type categorization and creating an automated, streamlined system for error incidents. In this paper, we demonstrate text-classification models developed with clinical data from a full service radiation oncology center (test center) that can predict the broad level and first level category of an error given a free-text description of the error. All but one of the resulting models had an excellent performance as quantified by several metrics. The results also suggest that more development and more extensive training data would further improve future results.
医学研究所2000年发表的一份报告显示，医疗差错是患者死亡的主要原因之一，并敦促开发差错检测和报告系统。放射肿瘤学领域因其高度复杂的流程工作流、各种系统、设备和医务人员之间大量的交互以及广泛的准备和治疗交付步骤，特别容易出现这些差错。自然语言处理（NLP）辅助的统计算法有潜力通过减轻人工报告者对事件类型分类的负担，并创建一个自动化、简化的差错事件系统，显著改善这些医疗差错的发现和报告。在本文中，我们展示了利用一家全方位服务的放射肿瘤学中心（测试中心）的临床数据开发的文本分类模型，该模型可以在给出差错的自由文本描述的情况下预测差错的大致级别和一级类别。除一个模型外，所有生成的模型在几个指标的量化下都表现出色。结果还表明，更多的开发和更广泛的训练数据将进一步改善未来的结果。
