来自PubMed数据库的"腹腔透析"案例报告文章的文本挖掘.
Kazuhiko Fukushima1, Kenji Tsuji1, Hiroyuki Nakanoh1
1Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
概括
人工智能 (AI) 文本挖掘可以有效地支持腹透析 (PD) 的日益增长领域的信息检索. 对PD病例报告的分析确定了关键趋势和主题,帮助研究人员.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 关于腹膜透析 (PD) 的医学文献数量越来越多,这给信息检索带来了挑战.
- 有效地访问和合成PD研究对于临床实践和进步至关重要.
研究的目的:
- 评估人工智能 (AI) 文本挖掘在腹腔透析 (PD) 中有效收集信息的实用性.
主要方法:
- 应用文本挖掘来分析来自PubMed (1970-2021) 的3137例腹膜透析 (PD) 病例报告的摘要.
- 方法包括提取术语的单词频率,依赖性和过渡分析.
主要成果:
- 提取了超过28万个相关单词,揭示了关键词,如腹膜炎,封装腹膜硬化症和儿童.
- 分析确定了历史趋势,并预测了PD的未来研究方向.
结论:
- 人工智能文本挖掘有效支持腹透析 (PD) 研究中的有效信息收集和趋势分析.
- 这种方法有助于导航不断扩大的PD文献.
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