在临床决策支持中探索命名实体识别潜力和定制自然语言处理管道的价值:定量研究
Veysel Kocaman1, Fu-Yuan Cheng2, Julio Bonis1
1John Snow Labs Inc, Lewes, DE, United States.
JMIR AI
|September 5, 2025
概括
这项研究优化了临床注释的命名实体识别 (NER),在识别医疗术语及其在患者中的存在方面实现了高精度,改善了临床决策支持.
科学领域:
- 自然语言处理
- 临床信息学
- 生物医学数据科学
背景情况:
- 临床笔记包含非结构化的患者数据,由于医学术语和模糊性,对分析提出了挑战.
- 临床决策支持工具的实时数据提取因这种非结构性而复杂.
- 命名实体识别 (NER) 对于在临床文本中识别关键患者信息至关重要.
研究的目的:
- 评估临床笔记的命名实体识别 (NER) 管道的数据策划,技术和工作流程.
- 评估NER模型和NER断言模型在识别和分类临床实体中的性能.
- 通过增强自然语言处理,提高临床决策支持工具的准确性.
主要方法:
- 使用Spark NLP临床预训练的NER模型对来自5000名患者的138,250个临床笔记.
- 收集医疗进展,放射学和病理学笔记, 测量各种绩效指标.
- 根据专家的注释评估NER精度和断言模型精度/回忆.
主要成果:
- 实现了极好的NER精度,在程序中达到0.989的峰值,并且断言模型精度为0.889.
- 在票据特征和实体密度中确定了长尾分布.
- 与放射学和病理学笔记相比,进展护理笔记显示实体密度明显高.
结论:
- 专门的NER模型和管道设置对于不同类型的临床笔记至关重要.
- 进一步的研究应该包括各种临床注释,如出院摘要和精神评估.
- 定制NLP方法可以提高各种临床场景的性能.
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