患者电子健康记录的表征学提取和基于机器学习的分类:回顾性分析
Yilin Xia1, Mengqiao He1, Sijia Basang1
1Department of Neurology, West China Hospital, Sichuan University, #37 Guoxue Alley, Wuhou District, Chengdu, China, 86 18980605819.
JMIR medical informatics
|December 2, 2024
概括
这项研究引入了一种自动化系统来诊断,该系统使用自然语言处理和机器学习来从电子健康记录中提取发作的半理学,从而提高了分类准确性.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 医疗保健中的机器学习
背景情况:
- 精确的诊断依赖于高效的形形象学描述和分类.
- 目前的信息资源和的决策支持工具是不够的.
研究的目的:
- 开发一种症状实体提取工具和一种 semiology 实体学 (ESO).
- 通过机器学习实现的自动二进制分类.
主要方法:
- 使用 10,925 个病例的电子健康记录构建了一个 ESO 和一个症状实体提取工具.
- 采用自然语言处理技术来提取发作的特征,如持续时间,症状和频率.
- 利用机器学习模型,包括堆叠组合方法 (XGBoost,随机森林),用于自动化患者分类.
主要成果:
- 症状提取工具的准确率达到了85%.
- 堆叠组合模型产生了75.03%的F1得分.
- 随机森林模型显示了曲线下的高面积 (0.985).
结论:
- 证明了使用自然语言处理来从医疗记录中结构化提取病数据的可行性.
- 提供开放的本体资源,以支持未来的信息学研究.
关键词:
中国中国中国中国.华人中文 华人中文是什么意思决策支持工具 决策支持工具诊断 诊断 诊断 诊断 诊断 诊断电子健康记录 电子健康记录是一种.医疗记录 医疗记录机器学习是机器学习.自然语言处理自然语言处理.在本体论上,本体论是存在的.追溯分析是一种回顾性分析.发作 发作 发作半理学是什么意思 半理学是什么意思这是一个症状,一个症状.治疗治疗治疗治疗治疗治疗不结构化的文本.更多相关视频
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