在学习健康系统中开发基于机器学习的Mpox监控模型
Harry Reyes Nieva1,2, Jason Zucker1,3,4, Emma Tucker5
1Department of Biomedical Informatics, Columbia University, New York, NY, USA.
medRxiv : the preprint server for health sciences
|October 14, 2024
概括
开发了机器学习模型,以检测临床笔记中的mopox病例. 拉索回归被证明优于深度学习,有效减少虚假阳性,以提高质量.
科学领域:
- 计算流行病学计算流行病学
- 医疗信息学医学信息学
- 机器学习在医疗保健中的应用
背景情况:
- 准确及时识别mopox病例对于公共卫生监测和患者管理至关重要.
- 临床笔记包含用于疾病检测的宝贵信息,但需要复杂的分析方法.
- 学习健康系统旨在将数据驱动的见解融入临床实践,以实现持续改进.
研究的目的:
- 开发和评估机器学习和深度学习模型,以从非结构化的临床笔记中识别mpox病例.
- 为了比较不同建模方法的性能,特别是拉索回归和深度学习,在检测mpox.
- 评估这些模型在学习健康系统中的实用性,以提高质量.
主要方法:
- 机器学习 (拉索回归) 和深度学习模型的开发.
- 使用临床笔记数据集对模型进行培训和验证.
- 评估模型性能指标,重点关注准确性和错误阳性率.
主要成果:
- 与深度学习模型相比,拉索回归在识别mpox病例方面表现优越.
- 拉索回归模型在最大限度地减少假阳性识别方面特别有效.
- 开发的模型显示了将其整合到学习健康系统中的潜力.
结论:
- 机器学习,特别是拉索回归,为从临床笔记中识别mpox提供了一种可行和有效的方法.
- 拉索回归能够最大限度地减少假阳性,这使得它成为提高质量的宝贵工具,可能有助于检测错过或延迟的诊断.
- 这些发现支持在学习卫生系统中使用计算方法,以加强疾病监测和改善临床质量.
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