Classification of hospital acquired complications using temporal clinical information from a large electronic health record.
Classification of hospital acquired complications using temporal clinical information from a large electronic health record.
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DOI：
10.1016/j.jbi.2015.12.008
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发表时间：
2016-02
影响因子：
4.5
通讯作者：
Alterovitz G
中科院分区：
文献类型：
--
作者：
Warner JL;Zhang P;Liu J;Alterovitz G
医院获得性并发症（HACs）是影响现代医疗机构的严重问题。据估计，HAC导致美国医院的住院总成本增加约10%。由于美国医院每年的总支出接近9000亿美元，因此HAC造成的损失不是小事。早期发现和预防HAC可以大大减少美国医疗保健系统的压力，并改善患者发病率和死亡率。在这里，我们描述了一个机器学习模型，用于使用时间临床数据预测五个不同类别中的HAC的发生。使用我们的方法，我们发现，在采取有效的预防措施的情况下，仅在美国就可以节省至少100亿美元的过度医院费用。此外，我们还确定了几个关键特征，这些特征在患者入院后的不同时间段内表现出对HAC的高预测能力。本研究中分析的分类器和特征显示出很高的希望，能够用于准确预测临床环境中的HAC，并进一步提供了新的见解，了解各种临床因素对发展HAC风险的贡献，作为医疗系统暴露的函数。
Hospital Acquired Complications (HACs) are serious problems affecting modern day healthcare institutions. It is estimated that HACs result in an approximately 10% increase in total inpatient hospital costs across US hospitals. With US hospital spending totaling nearly $900 billion per annum, the damages caused by HACs are no small matter. Early detection and prevention of HACs could greatly reduce strains on the US healthcare system and improve patient morbidity & mortality rates. Here, we describe a machine-learning model for predicting the occurrence of HACs within five distinct categories using temporal clinical data. Using our approach, we find that at least $10 billion of excessive hospital costs could be saved in the US alone, with the institution of effective preventive measures. In addition, we also identify several keystone features that demonstrate high predictive power for HACs over different time periods following patient admission. The classifiers and features analyzed in this study show high promise of being able to be used for accurate prediction of HACs in clinical settings, and furthermore provide novel insights into the contribution of various clinical factors to the risk of developing HACs as a function of healthcare system exposure.