医学
数据科学
数据提取
围手术期
电子健康档案
信息抽取
人工智能
健康档案
自然语言处理
梅德林
外科
计算机科学
医疗保健
政治学
法学
经济
经济增长
作者
Miranda X. Morris,Ethan Y. Song,Aashish Rajesh,Nicolás M. Kass,Malke Asaad,Brett T. Phillips
标识
DOI:10.1177/00031348221117039
摘要
The vast and ever-growing volume of electronic health records (EHR) have generated a wealth of information-rich data. Traditional, non-machine learning data extraction techniques are error-prone and laborious, hindering the analytical potential of these massive data sources. Equipped with natural language processing (NLP) tools, surgeons are better able to automate, and customize their review to investigate and implement surgical solutions. We identify current perioperative applications of NLP algorithms as well as research limitations and future avenues to outline the impact and potential of this technology for progressing surgical innovation.
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