使用初始电子医疗记录进行诊断推理的定量分析
Shinya Takeuchi1, Yoshiyasu Okuhara2, Yutaka Hatakeyama2
1Department of Disaster and Emergency Medicine, Kochi Medical School, Kochi University, Nankoku 783-8505, Kochi, Japan.
Diagnostics (Basel, Switzerland)
|June 26, 2025
概括
最初的电子病历 (EMR) 包含了用于诊断推理的宝贵语言线索. 对EMR的文本分析可以揭示临床推理模式,有助于医学教育和诊断支持系统.
科学领域:
- 临床信息学 临床信息学
- 医学教育 医学教育
- 自然语言处理自然语言处理.
背景情况:
- 诊断推理至关重要,但往往是自动化和不透明的.
- 有限的定量研究评估电子病历 (EMR) 中的临床推理.
- 需要探索EMR以深入了解诊断过程.
研究的目的:
- 调查初始EMR是否包含诊断推理信息.
- 评估文本分析和逻辑回归,以可视化推理.
- 提高对临床决策的理解.
主要方法:
- 在科奇大学医院对EMR (2008-2022) 的回顾性分析.
- 使用日本NLP库 (GiNZA) 进行文本分析.
- 后勤回归用于将术语与头和头痛队列的最终诊断联系起来.
主要成果:
- 在248个头病例中确定了48个重要的诊断术语.
- 在616个头痛病例中确定了46个重要的诊断术语.
- 术语的存在和表达 (肯定/否定) 与诊断有显著的相关性.
结论:
- 最初的EMR包含可量化的语言数据,反映了诊断推理.
- 简单的分析方法可以揭示推理模式.
- 这些发现支持医学教育和诊断中的解释性AI.
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