利用电子健康记录对舒适死亡结果进行预测建模。

Muhammad Kamran Lodhi, Rashid Ansari, Yingwei Yao, Gail M Keenan, Diana J Wilkie, Ashfaq A Khokhar
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引用次数: 9

摘要

电子健康记录(EHR)系统在医疗保健行业中用于观察患者的病情进展。随着数据量的快速增长,电子病历数据分析已成为一个大数据问题。大多数电子病历都是稀疏的多维数据集,由于许多原因,挖掘它们是一项具有挑战性的任务。本文利用护理电子病历系统建立预测模型,以确定影响死亡焦虑的因素,这是临终患者的一个重要问题。不同的现有建模技术已被用于开发粗粒度和细粒度模型来预测患者的结果。粗粒度模型有助于预测每次住院结束时的结果,而细粒度模型有助于预测每次轮班结束时的结果,从而提供预测结果的轨迹。基于不同的建模技术,我们的结果显示出非常准确的预测,由于相对无噪声的数据。这些模型可以帮助确定有效的治疗方法,降低医疗保健成本,提高生命末期(EOL)护理的质量。
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Predictive Modeling for Comfortable Death Outcome Using Electronic Health Records.

Electronic health record (EHR) systems are used in healthcare industry to observe the progress of patients. With fast growth of the data, EHR data analysis has become a big data problem. Most EHRs are sparse and multi-dimensional datasets and mining them is a challenging task due to a number of reasons. In this paper, we have used a nursing EHR system to build predictive models to determine what factors impact death anxiety, a significant problem for the dying patients. Different existing modeling techniques have been used to develop coarse-grained as well as fine-grained models to predict patient outcomes. The coarse-grained models help in predicting the outcome at the end of each hospitalization, whereas fine-grained models help in predicting the outcome at the end of each shift, therefore providing a trajectory of predicted outcomes. Based on different modeling techniques, our results show significantly accurate predictions, due to relatively noise-free data. These models can help in determining effective treatments, lowering healthcare costs, and improving the quality of end-of-life (EOL) care.

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