Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing.
Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing.
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DOI：
10.3390/diagnostics13071215
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发表时间：
2023-03-23
期刊：
影响因子：
3.6
通讯作者：
Sengupta, Srijan
中科院分区：
文献类型：
--
作者：
Ganguly, Indrila;Buhrman, Graham;Kline, Ed;Mun, Seong K. K.;Sengupta, Srijan
医学研究所2000年发表的一份报告显示，医疗差错是病人死亡的主要原因，并敦促开发差错检测和报告系统。放射肿瘤学领域由于其高度复杂的流程工作流程，各种系统，设备和医务人员之间的大量交互以及广泛的准备和治疗实施步骤，特别容易受到这些错误的影响。自然语言处理（NLP）辅助统计算法有可能通过减轻人类报告者的事件类型分类负担并创建自动化，简化的错误事件系统来显着改善这些医疗错误的发现和报告。在本文中，我们展示了文本分类模型开发的临床数据，从一个全面的服务放射肿瘤中心（测试中心），可以预测广泛的水平和第一级类别的错误给出了一个自由文本描述的错误。除一个模型外，所有模型都具有出色的性能，可以通过几个指标进行量化。结果还表明，更多的开发和更广泛的培训数据将进一步改善未来的结果。
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.
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影响因子：
2.1
作者：
Siochi, R. Alfredo;Balter, Peter;Zhu, X. Ronald
通讯作者：
Zhu, X. Ronald
DOI：
10.1016/j.ijmedinf.2017.05.005
发表时间：
2017-08-01
影响因子：
4.9
作者：
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通讯作者：
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影响因子：
1.7
作者：
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通讯作者：
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影响因子：
3.3
作者：
Hartvigson, Pehr E.;Gensheimer, Michael F.;Ford, Eric C.
通讯作者：
Ford, Eric C.
影响因子：
3.9
作者：
Massafra, Raffaella;Fanizzi, Annarita;Amoroso, Nicola;Bove, Samantha;Comes, Maria Colomba;Pomarico, Domenico;Didonna, Vittorio;Diotaiuti, Sergio;Galati, Luisa;Giotta, Francesco;La Forgia, Daniele;Latorre, Agnese;Lombardi, Angela;Nardone, Annalisa;Pastena, Maria Irene;Ressa, Cosmo Maurizio;Rinaldi, Lucia;Tamborra, Pasquale;Zito, Alfredo;Paradiso, Angelo Virgilio;Bellotti, Roberto;Lorusso, Vito
通讯作者：
Lorusso, Vito