biostudies-literatureUnknown9(2)Zou H<h4>Background and aims</h4>Accurately predicting length of stay (LOS) is considered a challenging task for health care systems globally. In previous studies on LOS range prediction, researchers commonly pre-classified the LOS ranges, which were the same for all patients in the same classification, and then utilized a classifier for prediction. In this study, we innovatively aimed to predict the specific LOS range for each patient (the LOS range was different for each patient).<h4>Methods</h4>In the modified deep neural network (DNN), the overall sample error (root mean square error (RMSE) method), the estimated sample error (ERR_pred method), the probability distribution with different loss functions (Dis_pred_Loss1, Dis_pred_Loss2, and Dis_pred_Loss3 method), and the generative adversarial networks (WGAN-GP for LOS method) are used for LOS range prediction. The Medical Information Mart for Intensive Care III (MIMIC-III) database is used to validate these methods.<h4>Results</h4>The RMSE method is convenient for LOS range prediction, but the predicted ranges are all consistent in the same batch of samples. The ERR_pred method can achieve better prediction results in samples with low errors. However, the prediction effect is worse in samples with larger errors. The Dis_pred_Loss1 method encounters a training instability problem. The Dis_pred_Loss2 and Dis_pred_Loss3 methods perform well in making predictions. Although WGAN-GP for LOS method does not show a substantial advantage over other methods, this method might have the potential to improve the predictive performance.<h4>Conclusion</h4>The results show that it is possible to achieve an acceptable accurate LOS range prediction through a reasonable model design, which may help physicians in the clinic.Heliyone13573https://www.ebi.ac.uk/biostudies/studies/S-EPMC9958433biostudies-literaturePredicting length of stay ranges by using novel deep neural networks.PMC9958433Zou HWang MYang WTang LLiang HZhu QWu HfalsePredicting length of stay ranges by using novel deep neural networks.<h4>Background and aims</h4>Accurately predicting length of stay (LOS) is considered a challenging task for health care systems globally. In previous studies on LOS range prediction, researchers commonly pre-classified the LOS ranges, which were the same for all patients in the same classification, and then utilized a classifier for prediction. In this study, we innovatively aimed to predict the specific LOS range for each patient (the LOS range was different for each patient).<h4>Methods</h4>In the modified deep neural network (DNN), the overall sample error (root mean square error (RMSE) method), the estimated sample error (ERR_pred method), the probability distribution with different loss functions (Dis_pred_Loss1, Dis_pred_Loss2, and Dis_pred_Loss3 method), and the generative adversarial networks (WGAN-GP for LOS method) are used for LOS range prediction. The Medical Information Mart for Intensive Care III (MIMIC-III) database is used to validate these methods.<h4>Results</h4>The RMSE method is convenient for LOS range prediction, but the predicted ranges are all consistent in the same batch of samples. The ERR_pred method can achieve better prediction results in samples with low errors. However, the prediction effect is worse in samples with larger errors. The Dis_pred_Loss1 method encounters a training instability problem. The Dis_pred_Loss2 and Dis_pred_Loss3 methods perform well in making predictions. Although WGAN-GP for LOS method does not show a substantial advantage over other methods, this method might have the potential to improve the predictive performance.<h4>Conclusion</h4>The results show that it is possible to achieve an acceptable accurate LOS range prediction through a reasonable model design, which may help physicians in the clinic.2023-01-01T00:00:00Z2023 Feb2025-04-22T09:45:51.959Z2025-04-05T23:08:58.061ZS-EPMC99584333685202510.1016/j.heliyon.2023.e13573