• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Systems and Information Engineering Design Symposium >Prediction of Decompensation in Patients in the Cardiac Ward
【24h】

Prediction of Decompensation in Patients in the Cardiac Ward

机译：心脏病病人患者失代偿预测

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This study focuses on detecting deterioration of acutely ill patients in the cardiac ward at the University of Virginia Health System. Patients in the cardiac ward are expected to recover from a variety of cardiovascular procedures, but roughly 5% of patients deteriorate and have to be transferred to the Intensive Care Unit (ICU). Previous work has shown that early warning scores utilizing vitals signs and common lab results greatly lower morality for high risk patients. To build upon these results, data were collected over the course of two years from 71 beds in three cardiac-related wards at the University of Virginia Health System. In addition to information commonly collected for early warning scores, these data also contained continuous electrocardiography (ECG) telemetry data for all patients. Given that only one percent of observations are labeled as events, the F1 score was used as the primary metric to assess the performance of each model; area under the curve (AUC) was also considered. Previous work includes the development of logistic regression models with these data resulting in an AUC of 0.73. In this work, a super learner was built to further the study by stacking logistic regression, random forest, and gradient boosting models. Furthermore, a denoising auto-encoder was created to generate computer-derived features, the results of which were fed to machine learning models mentioned previously to predict patient deterioration. The logistic regression model built on existing and computer-generated features resulted in an F1 score of 0.1 and AUC of 0.7, which is comparable to previous models built on the same patient data set. The super learner had an improvement over existing logistic regression models, with an F1 score of 0.24 and AUC of 0.79.
机译：本研究致力于检测弗吉尼亚大学卫生系统心脏病患者急性病患者的恶化。心脏病病人的患者预计将从各种心血管程序中恢复，但大约5％的患者劣化，必须转移到重症监护单位（ICU）。以前的工作表明，利用威力迹象和普通实验室的预警成绩会导致高风险患者的道德较低。为了建立这些结果，在弗吉尼亚州卫生系统大学的三个心脏相关病房中从71张床上收集数据。除了用于预警评分的常用信息外，这些数据还包含所有患者的连续心电图（ECG）遥测数据。鉴于只有一个百分之一的观察结果被标记为事件，F1分数被用作评估每个模型性能的主要指标;还考虑了曲线下的区域（AUC）。以前的工作包括开发Logistic回归模型，这些数据导致AUC为0.73。在这项工作中，通过堆叠逻辑回归，随机森林和渐变升压模型来建立超学习者以进一步研究。此外，创建了一种去噪自动编码器以产生计算机导出的特征，其结果被馈送到先前提到的机器学习模型以预测患者劣化。基于现有和计算机生成的功能的逻辑回归模型导致F1分数为0.1和0.7，其与在同一患者数据集上构建的先前模型相当。超学习者对现有的逻辑回归模型有所改善，F1得分为0.24和0.79的AUC。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号