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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Length of stay prediction for ICU patients using individualized single classification algorithm
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Length of stay prediction for ICU patients using individualized single classification algorithm

机译：使用个性化单分类算法对ICU患者的预测长度

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摘要

Background and Objective: In intensive care units (ICUs), length of stay (LOS) prediction is critical to help doctors and nurses select appropriate treatment options and predict patients' condition. Considering that most hospitals use universal models to predict patients' condition, which cannot meet the individual needs of special ICU patients. Our goal is to create a personalized model for patients to determine the number of hospital stays. Methods: In this study, a new combination of just-in-time learning (JITL) and one-class extreme learning machine (one-class ELM) is proposed to predict the number of days a patient stays in hospital. This combination is shortened as one-class JITL-ELM, where JITL is used to search for personalized cases for a new patient and one-class ELM is used to determine whether the patient can be discharged within 10 days. Results: The experimental results show that the one-class JITL-ELM model has an area under the curve (AUC) index of 0.8510, lift value of 2.1390, precision of 1, and G-mean is 0.7842. Its accuracy, specificity, and sensitivity were found as 0.82, 1, and 0.6150, respectively. Moreover, a novel simple mortality risk level estimation system that can determine the mortality rate of a patient by combining LOS and age is proposed. It has an accuracy rate of 66% and the miss rate of only 6.25%. Conclusions: Overall, the one-class JITL-ELM can accurately predict hospitalization days and mortality using early physiological parameters. Moreover, a simple mortality risk level estimation system based on a combination of LOS and age is proposed; the system is simple, highly interpretable, and has strong application value. (C) 2019 Published by Elsevier B.V.
机译：背景和目的：在重症监护单位（ICU）中，逗留时间（LOS）预测对于帮助医生和护士选择合适的治疗方案并预测患者病症至关重要。考虑到大多数医院使用普遍模型来预测患者的病情，这不能满足特殊ICU患者的个性化需求。我们的目标是为患者创建个性化模型，以确定医院住宿的数量。方法：在本研究中，提出了一种新的即时学习（JITL）和单级极端学习机（单级ELM）的新组合，以预测患者在医院停留的天数。这种组合被缩短为单级JITL-ELM，其中JITL用于搜索新患者的个性化案例，并且单级ELM用于确定患者是否可以在10天内放电。结果：实验结果表明，单级JITL-ELM模型在0.8510的曲线（AUC）指数下有一个区域，升力值为2.1390，精度为1，G平均值为0.7842。其精度，特异性和敏感性分别为0.82,1和0.6150。此外，提出了一种新的简单死亡率风险水平估计系统，可以通过组合LOS和年龄来确定患者的死亡率率。精度率为66％，未错过率仅为6.25％。结论：总体而言，单级JITL-ELM可以使用早期生理参数准确预测住院日和死亡率。此外，提出了基于LOS和年龄组合的简单死亡风险估计系统;该系统简单，高度可解释，具有强大的应用价值。 （c）2019年由elestvier b.v发布。

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