Alsinglawi Belal, Alnajjar Fady, Mubin Omar, Novoa Mauricio, Alorjani Mohammed, Karajeh Ola, Darwish Omar
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5442-5445. doi: 10.1109/EMBC44109.2020.9175889.
Predicting Cardiovascular Length of stay based hospitalization at the time of patients' admitting to the coronary care unit (CCU) or (cardiac intensive care units CICU) is deemed as a challenging task to hospital management systems globally. Recently, few studies examined the length of stay (LOS) predictive analytics for cardiovascular inpatients in ICU. However, there are almost scarcely real attempts utilized machine learning models to predict the likelihood of heart failure patients length of stay in ICU hospitalization. This paper introduces a predictive research architecture to predict Length of Stay (LOS) for heart failure diagnoses from electronic medical records using the state-of-art- machine learning models, in particular, the ensembles regressors and deep learning regression models. Our results showed that the gradient boosting regressor (GBR) outweighed the other proposed models in this study. The GBR reported higher R-squared value followed by the proposed method in this study called Staking Regressor. Additionally, The Random forest Regressor (RFR) was the fastest model to train. Our outcomes suggested that deep learning-based regressor did not achieve better results than the traditional regression model in this study. This work contributes to the field of predictive modelling for electronic medical records for hospital management systems.
在患者入住冠心病监护病房（CCU）或心脏重症监护病房（CICU）时预测心血管疾病住院时长，对全球医院管理系统而言是一项具有挑战性的任务。最近，很少有研究对重症监护病房中心血管疾病住院患者的住院时长预测分析进行探究。然而，几乎没有实际尝试利用机器学习模型来预测心力衰竭患者在重症监护病房住院的时长。本文引入了一种预测研究架构，使用先进的机器学习模型，特别是集成回归器和深度学习回归模型，从电子病历中预测心力衰竭诊断患者的住院时长。我们的结果表明，梯度提升回归器（GBR）在本研究中优于其他提出的模型。GBR报告的R平方值更高，其次是本研究中提出的名为堆叠回归器的方法。此外，随机森林回归器（RFR）是训练速度最快的模型。我们的结果表明，在本研究中基于深度学习的回归器没有比传统回归模型取得更好的结果。这项工作有助于医院管理系统电子病历预测建模领域的发展。
