在学习健康系统中开发基于机器学习的MOPOX监测模型
Harry Reyes Nieva1,2,3, Jason Zucker4,3, Emma Tucker5
1Department of Biomedical Informatics, Columbia University, New York, New York, USA harry.reyes@columbia.edu.
Sexually transmitted infections
|May 3, 2025
概括
机器学习和深度学习模型可以使用临床笔记检测mpox病例. 与LASSO规范化的后勤回归是最有效的,在mpox监控中表现优于深度学习模型.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 流行病学监测 流行病学监测
背景情况:
- 对于公共卫生而言,mpox (麻疹) 监测至关重要.
- 开发用于早期检测的自动化方法可以加强响应工作.
- 临床笔记包含有价值的信息,用于识别传染病病例.
研究的目的:
- 开发和评估机器学习 (ML) 和深度学习 (DL) 模型,以使用临床笔记检测mpox病例.
- 为了比较不同ML / DL模型在识别mopox监测中的性能.
主要方法:
- 一个主要医疗中心的临床遭遇的回顾性研究.
- 训练了三个模型:LASSO回归,临床BERT和临床Longformer.
- 通过使用精度,回忆,F1分数,AUROC,AUPRC和RP80.0来评估模型.
主要成果:
- 拉索回归显示出比DL模型更优越的性能.
- 拉索实现了0.93的精度,回忆和F1得分,AUROC为0.97.
- 与症状相关的短语是LASSO模型中的关键预测因素.
结论:
- ML和DL模型对mpox病例的检测和监测有希望.
- 拉索回归证明有效地减少了假阳性,超过了DL模型.
- 这些计算方法可以支持传染病监测和质量改善.
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