TY - JOUR AU - Zheng, Yaguang AU - Dickson, Victoria Vaughan AU - Blecker, Saul AU - Ng, Jason M AU - Rice, Brynne Campbell AU - Melkus, Gail D 'Eramo AU - Shenkar, Liat AU - Mortejo, Marie Claire R AU - Johnson, Stephen B PY - 2022 DA - 2022/5/16 TI -使用自然语言处理识别低血糖患者:系统文献综述JO - JMIR糖尿病SP - e34681 VL - 7 IS - 2kw -低血糖KW -自然语言处理KW -电子健康记录KW -糖尿病AB -背景:准确识别低血糖患者是预防不良事件和死亡的关键。自然语言处理(NLP)是人工智能的一种形式，它使用计算算法从文本数据中提取信息。NLP是一种可扩展的、高效的、快速的方法，用于从大量人群中使用电子健康记录数据源提取低血糖相关信息。目的:对应用NLP从电子病历临床记录中提取低血糖的文献进行系统综述。方法:电子检索PubMed、Web of Science Core Collection、CINAHL (EBSCO)、PsycINFO (Ovid)、IEEE Xplore、谷歌Scholar和ACL Anthology。关键词包括低血糖、低血糖、NLP、机器学习。纳入标准包括应用NLP识别低血糖，报告与低血糖相关的结果，并以英文全文发表的研究。结果:本综述(n=8项研究)揭示了与低血糖相关的报道结果的异质性。纳入的8项研究中，4项(50%)报告任何水平低血糖的患病率为3.4%至46.2%。 The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. Conclusions: The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing. SN - 2371-4379 UR - https://diabetes.www.mybigtv.com/2022/2/e34681 UR - https://doi.org/10.2196/34681 UR - http://www.ncbi.nlm.nih.gov/pubmed/35576579 DO - 10.2196/34681 ID - info:doi/10.2196/34681 ER -