临床叙述中的O2补充含糊性,以支持回顾性COVID-19研究
Akhila Abdulnazar1,2, Amila Kugic1, Stefan Schulz1
1Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.
自动分类器可以从出院总结中识别需要氧气的COVID-19患者. 经典的机器学习模型以较低的计算成本提供与深度学习相似的性能.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 机器学习 机器学习
背景情况:
- 氧和是COVID-19严重程度的一个关键指标,静音低氧症会带来诊断挑战.
- 电子健康记录 (EHR) 包含临床笔记中的重要氧化数据.
- 需要补充氧气的患者的自动识别对于COVID-19研究和减少医生工作量至关重要.
研究的目的:
- 开发和比较自动分类器,以识别从德国出院总结中接受氧气补充的COVID-19患者.
- 为此分类任务评估经典机器学习和深度学习模型的性能.
- 为了利用本地可解释的模型不可知解释 (LIME) 来实现模型的解释性.
主要方法:
- 德国COVID-19患者出院摘要的匿名分析.
- 使用各种机器学习 (ML) 和深度学习 (DL) 算法进行二进制分类.
- 应用LIME以可视化和解释标记级别的分类器决策.
主要成果:
- 经典的ML和DL模型都实现了高分类性能 (F-措施0.942-0.955).
- 与DL模型相比,经典ML方法显示了更快的处理时间.
- LIME可视化揭示了不同的编码模式,并突出了影响模型预测的关键特征.
结论:
- 经典的ML方法提供了与DL模型相比的性能,用于氧补充分类,并降低了计算成本.
- 通过文本和视觉解释,LIME提供了对模型行为和性能的有价值的定性见解.
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