学习型健康系统中基于机器学习的猴痘监测模型的开发
Development of Machine Learning-Based Mpox Surveillance Models in a Learning Health System.
作者信息
Nieva Harry Reyes, Zucker Jason, Tucker Emma, McLean Jacob, DeLaurentis Clare, Gunaratne Shauna, Elhadad Noémie
机构信息
Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Department of Medicine, Harvard Medical School, Boston, MA, USA.
出版信息
medRxiv. 2024 Sep 27:2024.09.25.24314318. doi: 10.1101/2024.09.25.24314318.
We developed machine learning and deep learning models to identify mpox cases from clinical notes as part of a learning health system initiative. Lasso regression outperformed deep learning models, excelled in minimizing false positives, and may prove helpful for flagging missed or delayed diagnoses as part of continuous quality improvement.
作为学习型健康系统计划的一部分，我们开发了机器学习和深度学习模型，用于从临床记录中识别猴痘病例。套索回归优于深度学习模型，在将假阳性降至最低方面表现出色，并且作为持续质量改进的一部分，可能有助于标记漏诊或延迟诊断。
Zotero 插件