从临床笔记中对婴儿早期养状况进行分类,使用自然语言处理和机器学习
Dominick J Lemas1,2, Xinsong Du3,4, Masoud Rouhizadeh5,6
1Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Clinical and Translational Research Building, Gainesville, FL, 32610, USA. djlemas@ufl.edu.
Scientific reports
|April 3, 2024
概括
这项研究开发了自然语言处理 (NLP) 模型,从电子健康记录中准确预测婴儿养状态. XGBoost模型的准确率达到90.1%,使得公共卫生干预措施的早期识别成为可能.
科学领域:
- 计算语言学计算语言学
- 医疗信息学 医疗信息学
- 机器学习是机器学习.
背景情况:
- 准确的婴儿养状态对于公共健康至关重要.
- 电子健康记录 (EHR) 包含有价值的非结构化的临床笔记.
- 从电子健康记录中预测养状况可以为目标干预提供信息.
研究的目的:
- 开发和评估自然语言处理 (NLP) 和机器学习 (ML) 模型.
- 用Epic EHR系统的临床笔记来预测婴儿养状态.
- 使用医疗主体标题 (MeSH) 术语对养状态进行分类.
主要方法:
- 在999个手动审查的临床笔记上训练了6个ML模型 (逻辑回归,随机森林,XGBoost,k-NN,SVC).
- 使用TeamTat进行注释和MeSH术语进行分类.
- 基于准确性,精度,回忆和F1分数的评估模型.
主要成果:
- XGBoost模型以90.1%的准确性展示了卓越的性能.
- 实现了宏观平均精度,回忆和F1分数分别为90.3%,90.1%和90.1%.
- 成功地将婴儿养状态分为母乳,配方奶粉/奶瓶和缺少的类别.
结论:
- 根据非结构化EHR数据,NLP有效地对婴儿养状况进行分类.
- 通过NLP早期识别可以支持精确的公共卫生倡议.
- 这些发现可以帮助改善产后患者的哺乳支持.
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