Key result
Gradient boosting regressor outperforms other machine learning models in predicting ICU LOS for HF patients.
Why the study?
Predicting length of stay upon CCU or CICU admission is challenging, and few studies have utilized machine learning models to predict ICU length of stay specifically for heart failure patients.
Can machine learning models accurately predict the length of stay for heart failure patients in the ICU?
Can machine learning models accurately predict the length of stay for heart failure patients in the ICU?
Gradient boosting regressors provide superior performance compared to deep learning models for predicting ICU length of stay in heart failure patients using electronic medical records.
Hypothesis-generating for ML-based ICU length-of-stay forecasting in heart failure; requires prospective validation before clinical adoption.
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.
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Alsinglawi et al. (2020) studied Heart failure. Machine learning models vs. Other machine learning and deep learning models was evaluated on Length of Stay (LOS). Gradient boosting regressor outperformed other machine learning and deep learning models in predicting length of stay for heart failure patients in the intensive care unit.
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