开发一个LSTM模型,使用电子医疗记录识别手术部位感染
Amber C Kiser1, Karen Eilbeck1, Brian T Bucher1,2
1Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT.
概括
一个新的长期短期记忆 (LSTM) 模型有效地使用电子健康记录识别手术部位感染 (SSIs). 这种自动监控方法提供了一个更有效的替代手动图表审查,以改善患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床监督 临床监督
背景情况:
- 手术部位感染 (SSI) 是造成手术并发症,再入院和医疗费用增加的重要原因.
- 传统的SSI监控手动图表审查是劳动密集型和昂贵的.
- 需要有效和自动化的方法来检测SSI.
研究的目的:
- 开发和评估一个长期短期记忆 (LSTM) 模型,用于使用电子健康记录 (EHR) 数据自动识别手术部位感染 (SSIs).
- 将LSTM模型的性能与SSI检测的传统机器学习方法进行比较.
主要方法:
- 开发一个使用结构化电子健康记录 (EHR) 数据的长期短期记忆 (LSTM) 模型.
- 培训和验证LSTM模型用于确定SSI的具体任务.
- 使用随机森林 (RF) 模型进行比较分析,这是传统的机器学习方法.
主要成果:
- 性能最高的LSTM模型实现了0.570的平均精度 (AP) 和0.905.90的接收器操作特征曲线 (AUROC) 下的面积.
- LSTM模型的表现优于顶级随机森林模型,该模型的AP为0.552和AUROC为0.899.
- 开发的LSTM模型在从EHR数据中识别SSI方面表现强.
结论:
- 该LSTM模型代表了手术部位感染 (SSI) 的自动监测的重大进步.
- 使用人工智能的自动化SSI监控可以增强医疗保健的质量改进机制.
- 这种方法为手工方法提供了更有效和潜在的成本效益更高的替代方案.
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