Prediction of Length of Stay on the Intensive Care Unit Based on Least Absolute Shrinkage and Selection Operator
Prediction of Length of Stay on the Intensive Care Unit Based on Least Absolute Shrinkage and Selection Operator
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DOI：
10.1109/access.2019.2934166
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Xu, Weifeng
中科院分区：
文献类型：
--
作者：
Li, Chunling;Chen, Longyi;Xu, Weifeng
重症监护室（ICU）的住院时间（LoS）是一种常见的结局指标，可用作护理质量和资源使用的指标。然而，现有的信息损耗分析方法缺乏可解释性和可扩展性，对信息损耗的预测性能存在争议。本文的研究包括2015年12月11日至2018年12月6日期间进入四川省人民医院ICU的1，214名计划外ICU入院患者的数据。在这些数据的基础上，本研究使用先进的预处理技术，探索性数据分析（EDA）和最小绝对收缩和选择算子（LASSO）算法创建了一个高度准确和预测的模型。接下来，本研究通过10倍交叉验证和外部验证方法，使用均方根预测误差（RMSPE），平均绝对误差（MAE）和决定系数（R-2）评估所提出的模型的预测性能。所提出的模型的预测性能为RMSPE的0.88 +/- 0.13天，MAE的0.87 +/- 0.07天和R-2的0.35 +/- 0.09。实验结果表明，该方法的性能与国家的最先进的方法和结果具有竞争力。此外，本研究还探讨了幸存者和非幸存者ICU LoS的危险因素，并比较了其预测性能。
Length of stay (LoS) in the intensive care unit (ICU) is a common outcome measure used as an indicator of both quality of care and resource use. However, the existing analysis methods of LoS are poorly interpretable and extensible, and there is controversial for the predictive performance of LoS. In this paper, the study includes data from 1,214 unplanned ICU admissions to participate in the ICU of Sichuan Provincial People's Hospital between Dec. 11, 2015 and Dec. 6, 2018. On the basis of these data, this study creates a highly accurate and predictive model using advanced preprocessing techniques, exploratory data analysis (EDA) and least absolute shrinkage and selection operator (LASSO) algorithm. Next, this study evaluates the predictive performance of the proposed model by 10-fold cross validation and external validation method using the root mean square prediction error (RMSPE), mean absolute error (MAE), and coefficient of determination (R-2). The predictive performance of the proposed model is 0.88 +/- 0.13 day for RMSPE, 0.87 +/- 0.07 day for MAE and 0.35 +/- 0.09 for R-2. Experimental results show that the performance of the proposed method are competitive with the state-of-the-art methods and results. Furthermore, this study explores the risk factors for ICU LoS in survivors and non-survivors and compare their predictive performance.