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
PubMed
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.

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