12277Duke University School of Medicine, Durham, NC, USA.
Duke Pratt School of Engineering, Durham, NC, USA.
Am Surg. 2023 Jan;89(1):43-48. doi: 10.1177/00031348221117039. Epub 2022 Aug 15.
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.
电子健康记录（EHR）的数量庞大且不断增长，产生了大量丰富信息的数据。传统的、非机器学习的数据提取技术容易出错且繁琐，阻碍了这些大数据源的分析潜力。配备了自然语言处理（NLP）工具，外科医生能够更好地实现自动化，并根据需要自定义审查，以调查和实施手术解决方案。我们确定了 NLP 算法在当前围手术期的应用，以及研究限制和未来的途径，以概述这项技术对推进手术创新的影响和潜力。
