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自然语言处理在胃肠病学中的作用演变及未来方向。

Evolving Role and Future Directions of Natural Language Processing in Gastroenterology.

机构信息

Department of Gastroenterology and Hepatology, University of Missouri-Kansas City School of Medicine, 5000 Holmes Street, Kansas City, MO, 64110, USA.

Division of Health Services and Outcomes Research, Children's Mercy Kansas City, Kansas City, MO, USA.

出版信息

Dig Dis Sci. 2021 Jan;66(1):29-40. doi: 10.1007/s10620-020-06156-y. Epub 2020 Feb 27.

DOI:10.1007/s10620-020-06156-y
PMID:32107677
Abstract

In line with the current trajectory of healthcare reform, significant emphasis has been placed on improving the utilization of data collected during a clinical encounter. Although the structured fields of electronic health records have provided a convenient foundation on which to begin such efforts, it was well understood that a substantial portion of relevant information is confined in the free-text narratives documenting care. Unfortunately, extracting meaningful information from such narratives is a non-trivial task, traditionally requiring significant manual effort. Today, computational approaches from a field known as Natural Language Processing (NLP) are poised to make a transformational impact in the analysis and utilization of these documents across healthcare practice and research, particularly in procedure-heavy sub-disciplines such as gastroenterology (GI). As such, this manuscript provides a clinically focused review of NLP systems in GI practice. It begins with a detailed synopsis around the state of NLP techniques, presenting state-of-the-art methods and typical use cases in both clinical settings and across other domains. Next, it will present a robust literature review around current applications of NLP within four prominent areas of gastroenterology including endoscopy, inflammatory bowel disease, pancreaticobiliary, and liver diseases. Finally, it concludes with a discussion of open problems and future opportunities of this technology in the field of gastroenterology and health care as a whole.

摘要

与当前医疗改革的轨迹一致,人们非常重视提高临床就诊过程中收集的数据的利用。虽然电子健康记录的结构化字段为开始此类工作提供了一个方便的基础,但人们清楚地知道,大量相关信息都局限在记录护理的自由文本叙述中。不幸的是,从这些叙述中提取有意义的信息是一项非平凡的任务,传统上需要大量的人工努力。如今,来自自然语言处理 (NLP) 领域的计算方法有望在医疗保健实践和研究中对这些文档的分析和利用产生变革性的影响,特别是在胃肠病学 (GI) 等以程序为主的子学科中。因此,本文提供了一个专注于胃肠病学实践中的 NLP 系统的临床综述。它首先详细概述了 NLP 技术的现状,介绍了临床环境和其他领域的最新方法和典型用例。接下来,它将围绕 NLP 在四个主要的胃肠病学领域(包括内窥镜、炎症性肠病、胰胆和肝脏疾病)中的当前应用进行全面的文献综述。最后,它将讨论该技术在胃肠病学领域和整个医疗保健领域的开放性问题和未来机遇。

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Front Med (Lausanne). 2025 Jan 22;11:1512824. doi: 10.3389/fmed.2024.1512824. eCollection 2024.
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A foundation systematic review of natural language processing applied to gastroenterology & hepatology.一项关于应用于胃肠病学和肝病学的自然语言处理的基础系统评价。
BMC Gastroenterol. 2025 Feb 6;25(1):58. doi: 10.1186/s12876-025-03608-5.
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Advocating for Patients With Inflammatory Bowel Disease: How to Navigate the Prior Authorization Process.倡导炎症性肠病患者:如何应对预先授权流程。
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Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.
人工智能在肝脏疾病管理中的作用。
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Artificial Intelligence and the Future of Gastroenterology and Hepatology.人工智能与胃肠病学和肝病学的未来
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Identification of pancreatic cancer risk factors from clinical notes using natural language processing.利用自然语言处理从临床记录中识别胰腺癌风险因素。
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Large language models: a primer and gastroenterology applications.大语言模型:入门介绍及胃肠病学应用
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A deep learning and natural language processing-based system for automatic identification and surveillance of high-risk patients undergoing upper endoscopy: A multicenter study.一种基于深度学习和自然语言处理的系统,用于对上消化道内镜检查的高危患者进行自动识别和监测:一项多中心研究。
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