Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas; Department of Medicine, Baylor College of Medicine, Houston, Texas.
Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana; Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana; Department of Biomedical Informatics, Regenstrief Institute, LLC, Indianapolis, Indiana.
Clin Gastroenterol Hepatol. 2014 Aug;12(8):1257-61. doi: 10.1016/j.cgh.2014.05.013. Epub 2014 May 21.
Natural language processing (NLP) is a technology that uses computer-based linguistics and artificial intelligence to identify and extract information from free-text data sources such as progress notes, procedure and pathology reports, and laboratory and radiologic test results. With the creation of large databases and the trajectory of health care reform, NLP holds the promise of enhancing the availability, quality, and utility of clinical information with the goal of improving documentation, quality, and efficiency of health care in the United States. To date, NLP has shown promise in automatically determining appropriate colonoscopy intervals and identifying cases of inflammatory bowel disease from electronic health records. The objectives of this review are to provide background on NLP and its associated terminology, to describe how NLP has been used thus far in the field of digestive diseases, and to identify its potential future uses.
自然语言处理 (NLP) 是一种使用基于计算机的语言学和人工智能技术从自由文本数据源(如进展记录、程序和病理学报告以及实验室和放射学检查结果)中识别和提取信息的技术。随着大型数据库的创建和医疗保健改革的轨迹,NLP 有望通过提高临床信息的可用性、质量和实用性来改善美国的文档、质量和医疗保健效率。迄今为止,NLP 在自动确定适当的结肠镜检查间隔和从电子健康记录中识别炎症性肠病病例方面显示出了前景。本综述的目的是提供 NLP 及其相关术语的背景信息,描述 NLP 在消化疾病领域迄今为止的应用,并确定其潜在的未来用途。