巴雷特食道的自动决策:开发和部署自然语言处理工具

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Agathe Zecevic, Laurence Jackson, Xinyue Zhang, Polychronis Pavlidis, Jason Dunn, Nigel Trudgill, Shahd Ahmed, Pierfrancesco Visaggi, Zanil YoonusNizar, Angus Roberts, Sebastian S. Zeki
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引用次数: 0

摘要

人工决定对前期恶性巴雷特食管(BO)进行监测内镜检查的时间很容易出错。这导致了资源利用效率低下和安全风险。为了实现决策自动化,我们对来自盖伊和圣托马斯医院(Guy's and St Thomas' Hospital,GSTT)的 4831 份内窥镜检查报告和 4581 份病理报告中的巴雷特食管长度(EndoBERT)和最差组织病理学分级(PathBERT)进行了微调双向变压器编码器表征(Bidirectional Encoder Representations from Transformers,BERT)模型分类。来自 GSTT、国王学院医院(KCH)以及桑德维尔和西伯明翰医院(SWB)的 EndoBERT 测试集的准确率分别为 0.95、0.86 和 0.99。PathBERT 的平均准确率分别为 0.93、0.91 和 0.92。对 1640 份 GSTT 报告的回顾性分析显示,内镜医师的决定与模型建议之间存在 27% 的差异。这项研究强调了基于 NLP 的软件在胃肠道疾病监测中的开发和应用,并在多个地点展示了其高性能。分析强调了自动化在提高临床决策的准确性和指南遵循性方面的潜在效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Automated decision making in Barrett’s oesophagus: development and deployment of a natural language processing tool

Automated decision making in Barrett’s oesophagus: development and deployment of a natural language processing tool

Automated decision making in Barrett’s oesophagus: development and deployment of a natural language processing tool
Manual decisions regarding the timing of surveillance endoscopy for premalignant Barrett’s oesophagus (BO) is error-prone. This leads to inefficient resource usage and safety risks. To automate decision-making, we fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models to categorize BO length (EndoBERT) and worst histopathological grade (PathBERT) on 4,831 endoscopy and 4,581 pathology reports from Guy’s and St Thomas’ Hospital (GSTT). The accuracies for EndoBERT test sets from GSTT, King’s College Hospital (KCH), and Sandwell and West Birmingham Hospitals (SWB) were 0.95, 0.86, and 0.99, respectively. Average accuracies for PathBERT were 0.93, 0.91, and 0.92, respectively. A retrospective analysis of 1640 GSTT reports revealed a 27% discrepancy between endoscopists’ decisions and model recommendations. This study underscores the development and deployment of NLP-based software in BO surveillance, demonstrating high performance at multiple sites. The analysis emphasizes the potential efficiency of automation in enhancing precision and guideline adherence in clinical decision-making.
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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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