通过自然语言处理增强的探测患者信息:一个自上而下的信息库分析
George Mastorakos1, Aditya Khurana1, Ming Huang2
1Mayo Clinic Alix School of Medicine, Mayo Clinic, Scottsdale, AZ, USA.
患者门户消息通常是关于活跃的症状或物流. 使用自然语言处理 (NLP) 分析这些信息可以通过更好地理解患者沟通来帮助提高临床效率.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 临床交流 临床沟通
背景情况:
- 患者越来越多地使用异步通信平台与医疗保健团队互动.
- 通过NLP自动化对患者信息的分类和分类可以显著提高临床效率.
- 理解患者生成的文本对于开发先进的NLP应用程序至关重要.
研究的目的:
- 用NLP方法来描述患者门户消息的内容.
- 提供患者文本数据的描述性分析.
- 为医疗保健提供先进的NLP应用程序的开发提供信息.
主要方法:
- 从心脏病学,皮肤病学和胃肠病学部门收集了大约3000条患者门户信息.
- 分类消息分为"活动症状"",物流"",处方"或"更新"类别.
- 使用命名实体识别 (NER) 与UMLS库来识别医疗概念和分析消息分布.
主要成果:
- 活跃的症状和后勤信息占总体的67%左右.
- "发现"是信息类型和部门中最常见的医疗概念.
- 特定的关键词如"解剖学网站"和"疾病"在"活跃症状"消息中很常见,而"药物"在"处方"消息中很普遍.
结论:
- 对患者门户信息的描述性分析揭示了关键的内容主题和变化.
- 对消息内容差异的洞察力可以指导创建更有效的NLP模型.
- 这项研究为改善自动化消息分拣和临床工作流程提供了基础.
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