电子健康记录中检查点抑制剂诱导结肠炎病例的加速管理。

IF 2.5 Q2 HEALTH CARE SCIENCES & SERVICES
Protiva Rahman, Cheng Ye, Kathleen F Mittendorf, Michele Lenoue-Newton, Christine Micheel, Jan Wolber, Travis Osterman, Daniel Fabbri
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引用次数: 0

摘要

目的:自动识别有免疫检查点抑制剂(ICI)诱导结肠炎风险的患者,使医生能够改善患者护理。然而,预测模型需要来自电子健康记录(EHR)的培训数据。我们的目标是自动识别记录ici -结肠炎病例的笔记,以加速数据管理。材料和方法:我们提出了一个数据管道,从电子病历记录中自动识别ici -结肠炎,加速图表审查。该管道依赖于BERT,一种最先进的自然语言处理(NLP)模型。管道的第一阶段使用通过逻辑分类器识别的关键字分段长音符,并应用BERT识别ici -结肠炎音符。下一阶段使用第二个BERT模型来识别假阳性音符,并删除可能为提到结肠炎作为副作用而呈阳性的音符。最后一个阶段通过突出显示笔记中与结肠炎相关的部分来进一步加速整理。具体来说,我们使用BERT的注意力评分来找到描述结肠炎的高密度区域。结果:整个管道识别结肠炎笔记的准确率为84%,并将管理员笔记审查工作量减少了75%。片段BERT分类器具有0.98的高召回率,这对于识别低发生率至关重要(讨论:从EHR笔记中进行分类是一项繁重的任务,特别是当分类主题很复杂时。本工作中描述的方法不仅对ICI结肠炎有用,而且可以适用于其他领域。结论:我们的提取管道减少了人工笔记审查的工作量,使电子病历数据更易于研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Accelerated curation of checkpoint inhibitor-induced colitis cases from electronic health records.

Accelerated curation of checkpoint inhibitor-induced colitis cases from electronic health records.

Accelerated curation of checkpoint inhibitor-induced colitis cases from electronic health records.

Accelerated curation of checkpoint inhibitor-induced colitis cases from electronic health records.

Objective: Automatically identifying patients at risk of immune checkpoint inhibitor (ICI)-induced colitis allows physicians to improve patientcare. However, predictive models require training data curated from electronic health records (EHR). Our objective is to automatically identify notes documenting ICI-colitis cases to accelerate data curation.

Materials and methods: We present a data pipeline to automatically identify ICI-colitis from EHR notes, accelerating chart review. The pipeline relies on BERT, a state-of-the-art natural language processing (NLP) model. The first stage of the pipeline segments long notes using keywords identified through a logistic classifier and applies BERT to identify ICI-colitis notes. The next stage uses a second BERT model tuned to identify false positive notes and remove notes that were likely positive for mentioning colitis as a side-effect. The final stage further accelerates curation by highlighting the colitis-relevant portions of notes. Specifically, we use BERT's attention scores to find high-density regions describing colitis.

Results: The overall pipeline identified colitis notes with 84% precision and reduced the curator note review load by 75%. The segment BERT classifier had a high recall of 0.98, which is crucial to identify the low incidence (<10%) of colitis.

Discussion: Curation from EHR notes is a burdensome task, especially when the curation topic is complicated. Methods described in this work are not only useful for ICI colitis but can also be adapted for other domains.

Conclusion: Our extraction pipeline reduces manual note review load and makes EHR data more accessible for research.

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来源期刊
JAMIA Open
JAMIA Open Medicine-Health Informatics
CiteScore
4.10
自引率
4.80%
发文量
102
审稿时长
16 weeks
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