在社交媒体上关注糖尿病的健康消费者的普通词汇中识别医疗概念和语义类型,使用UMLS和NLP
Adib Ahmed Anik1, Paramita Basak Upama1, Masud Rabbani1
1Ubicomp Lab, Department of Computer Science, Marquette University, Milwaukee, WI, USA.
概括
这项研究使用自然语言处理从社交媒体中提取健康概念,帮助了解患者行为,并开发糖尿病等慢性疾病的健康应用程序.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 社交媒体分析
背景情况:
- 分析社交媒体上的健康消费者语言对于理解健康行为至关重要.
- 现有的医学本体学和NLP技术可用于此分析.
研究的目的:
- 开发一种方法来从社交媒体上的非专业人士健康讨论中提取和分类医疗概念.
- 评估NLP工具在将非正式健康术语映射到标准化医疗概念中的效率.
主要方法:
- 利用SpaCy和马科夫-维特比算法进行自然语言处理.
- 应用医学本体学 (UMLS) 来将提取的普通医学术语映射到定义的概念中.
- 专注于Tumblr上的糖尿病相关讨论作为一个案例研究.
主要成果:
- 成功地从普通人的词汇中提取了主要的医疗概念.
- 证明了在本体学中基于语义类型对概念进行分组的能力.
- 展示了系统处理单词模糊性和提取生活方式指标的能力.
结论:
- 开发的系统有效地分析来自社交媒体的自由文本,以识别与健康有关的信息.
- 这种方法可以增强监测糖尿病等慢性疾病的健康应用.
- 促进研究人员对健康消费者的语言和行为的理解.
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