在糖尿病管理面试中确定主要主题 使用自然语言处理-基于文本挖掘
1Author Affiliations: College of Nursing, Chungnam National University, Daejeon, Republic of Korea (Dr Cha); Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA (Dr Cha); and College of Nursing, Chonnam National University, Gwangju, Republic of Korea (Dr Lee).
Computers, informatics, nursing : CIN
|March 7, 2024
概括
患者采访的文本挖掘揭示了2型糖尿病教育计划的关键主题,重点是血糖控制,运动和以患者为中心的护理的数字工具使用.
科学领域:
- 医疗信息学 医疗信息学
- 卫生教育卫生教育教育
- 自然语言处理自然语言处理.
背景情况:
- 2型糖尿病的管理需要有效的患者教育.
- 了解患者体验对于程序改进至关重要.
研究的目的:
- 在糖尿病教育计划后,从成年患者的退场采访中确定关键主题.
- 用文本挖掘来探索患者的满意度和经验.
主要方法:
- 分析了2型糖尿病患者的18个半结构化的退场采访.
- 应用四种基于自然语言处理 (NLP) 的文本挖掘技术.
- 使用术语频率,反向文档频率,N-gram分析,以及代的融合关系 (CONCOR).
主要成果:
- 发现的最强的词汇关联是"糖尿病"和"教育" (N-gram分析).
- 康科尔分析确定了三个主要集群:血糖控制,运动和数字设备使用.
- 确定了以患者为中心的主题,以优化糖尿病教育计划.
结论:
- 文本挖掘提供了一种新的方法来可视化和分析来自患者访谈的定性数据.
- 研究结果强调了在糖尿病教育中整合血糖控制,运动和数字工具的重要性.
- 这种方法可以为开发更加以患者为中心的糖尿病教育策略提供信息.
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