Brigham and Women's Hospital, Boston, MA, USA.
J Diabetes Sci Technol. 2021 May;15(3):553-560. doi: 10.1177/19322968211000831. Epub 2021 Mar 19.
Real-world evidence research plays an increasingly important role in diabetes care. However, a large fraction of real-world data are "locked" in narrative format. Natural language processing (NLP) technology offers a solution for analysis of narrative electronic data.
We conducted a systematic review of studies of NLP technology focused on diabetes. Articles published prior to June 2020 were included.
We included 38 studies in the analysis. The majority (24; 63.2%) described only development of NLP tools; the remainder used NLP tools to conduct clinical research. A large fraction (17; 44.7%) of studies focused on identification of patients with diabetes; the rest covered a broad range of subjects that included hypoglycemia, lifestyle counseling, diabetic kidney disease, insulin therapy and others. The mean F score for all studies where it was available was 0.882. It tended to be lower (0.817) in studies of more linguistically complex concepts. Seven studies reported findings with potential implications for improving delivery of diabetes care.
Research in NLP technology to study diabetes is growing quickly, although challenges (e.g. in analysis of more linguistically complex concepts) remain. Its potential to deliver evidence on treatment and improving quality of diabetes care is demonstrated by a number of studies. Further growth in this area would be aided by deeper collaboration between developers and end-users of natural language processing tools as well as by broader sharing of the tools themselves and related resources.
真实世界证据研究在糖尿病治疗中发挥着越来越重要的作用。然而，很大一部分真实世界数据是以叙述性格式“锁定”的。自然语言处理（NLP）技术为分析叙述性电子数据提供了一种解决方案。
我们对专注于糖尿病的 NLP 技术研究进行了系统回顾。纳入 2020 年 6 月前发表的文章。
我们纳入了 38 项分析研究。其中大多数（24 项；63.2%）仅描述了 NLP 工具的开发；其余则使用 NLP 工具进行临床研究。很大一部分（17 项；44.7%）的研究侧重于识别糖尿病患者；其余则涵盖了广泛的主题，包括低血糖、生活方式咨询、糖尿病肾病、胰岛素治疗等。所有可用 F 分数的研究平均值为 0.882。在更具语言复杂性概念的研究中，F 分数往往较低（0.817）。有 7 项研究报告了可能对改善糖尿病护理提供证据的结果。
尽管仍然存在挑战（例如在更具语言复杂性概念的分析中），但用于研究糖尿病的 NLP 技术研究正在迅速发展。一些研究表明，它在提供治疗证据和提高糖尿病护理质量方面具有潜力。该领域的进一步发展将得益于自然语言处理工具的开发人员和最终用户之间的更深入合作，以及更广泛地共享工具本身和相关资源。
