@Article{信息:doi 10.2196 / / medinform。6328年,作者= "郑,Le和王曰和郝Shiying和胫骨,安德鲁·Y和金Bo和非政府组织,安D和Jackson-Browne,麦地那和樵夫,丹尼尔·J和傅Tianyun张,林嘉欣和周,鑫和朱,审理和戴,多萝西和Yu, Yunxian郑,帮派和李徼McElhinney,脱B和斑鸠,德沃尔和阿尔弗雷德,肖恩T和斯登,弗兰克和西尔维斯特,卡尔·G和扩大,埃里克和凌,雪峰布鲁斯”,标题=“基于web的糖尿病患者人群健康管理实时病例查找:基于自然语言处理的全国电子病历算法的前瞻性验证”，期刊=“JMIR Med Inform”，年=“2016”，月=“11”，日=“11”，卷=“4”，数=“4”，页=“e37”，关键词=“电子病历;自然语言处理;糖尿病;背景:基于结构化病历的糖尿病病例查找并不能完全识别出以自由文本形式提供的与糖尿病相关的病史的糖尿病患者。人工检查图表已经被使用，但涉及高人工成本和长延迟。目的:本研究开发和测试一个基于web的糖尿病病例查找算法，使用结构化和非结构化电子病历(EMRs)。方法:本研究基于涵盖美国缅因州几乎所有卫生设施的健康信息交换(HIE) EMR数据库。使用记叙性临床记录，基于web的自然语言处理(NLP)病例发现算法采用回顾性(2012年7月1日至2013年6月30日)开发，使用hie相关设施的随机子集，然后用其余设施进行盲测。 The NLP-based algorithm was subsequently integrated into the HIE database and validated prospectively (July 1, 2013, to June 30, 2014). Results: Of the 935,891 patients in the prospective cohort, 64,168 diabetes cases were identified using diagnosis codes alone. Our NLP-based case finding algorithm prospectively found an additional 5756 uncodified cases (5756/64,168, 8.97{\%} increase) with a positive predictive value of .90. Of the 21,720 diabetic patients identified by both methods, 6616 patients (6616/21,720, 30.46{\%}) were identified by the NLP-based algorithm before a diabetes diagnosis was noted in the structured EMR (mean time difference = 48 days). Conclusions: The online NLP algorithm was effective in identifying uncodified diabetes cases in real time, leading to a significant improvement in diabetes case finding. The successful integration of the NLP-based case finding algorithm into the Maine HIE database indicates a strong potential for application of this novel method to achieve a more complete ascertainment of diagnoses of diabetes mellitus. ", issn="2291-9694", doi="10.2196/medinform.6328", url="http://medinform.www.mybigtv.com/2016/4/e37/", url="https://doi.org/10.2196/medinform.6328", url="http://www.ncbi.nlm.nih.gov/pubmed/27836816" }