# Predicting Hospital Admissions to Reduce Crowding in the Emergency Departments
## 5 Conclusions  
A gradient boosting machine is a powerful tool to use in binary outcome predictive models. The results show that data collected at the moment of arrival to the ED can be used to predict hospital admissions accurately and that a model including data from a comprehensive hospital network has a better predictive performance when compared to a similar model developed with data from one unique health center only. It discusses the huge potential that the application of the models obtained could have in fighting crowding in EDs by allowing for an early start of the bed allocation process, making it possible to do all the required procedures for admission simultaneously to the patient being visited by the doctor, instead of doing it in a sequential manner after the visit, which unnecessarily crowds ED rooms and generates a non-optimal use of the available resources in EDs.  
The predictive power of a model for the whole population sample, from 0 to 115 years of age, with the gradient boosting algorithm, is presented to train and test the predictive models in R, splitting the data in a 70/30 partition. Variable importance for each of the models was analyzed. Receiver operating characteristic curves were created, and the area under the curve was obtained from each of them as a measure of predictive performance. The goal was set to improve previous research studies by using a more comprehensive dataset and the GBM method to obtain an AUC of 0.87 or higher. This goal was achieved with the complete model, which yielded an AUC of 0.8938 (95% CI 0.8929-0.8948), beating the best performance found in the literature for the prediction of admissions at the time of arrival to the ED. Since this value was the highest AUC result observed in any previous research, it can be taken as an indicator that using a comprehensive dataset can indeed improve predictive performance for ED admissions. Future work could integrate the model using sensor-based systems, BIM, and Digital Twin technologies [39; 40] to create a fully automated solution for patients, avoiding long waiting times.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Complete** & **Pediatrics** & **Adults DS** & **Young** & **Old Adults** \\  & **DS** & **DS** & & **Adults DS** & **DS** \\ \hline AUC & 0.8938 & 0.8703 & 0.89112 & 0.89114 & 0.8514 \\ Accuracy & 0.9113 & 0.9577 & 0.8985 & 0.9292 & 0.8182 \\ Specificity & 0.9777 & 0.9959 & 0.9709 & 0.9875 & 0.9181 \\ Sensitivity & 0.3746 & 0.1577 & 0.4019 & 0.3116 & 0.5007 \\ PPV & 0.9267 & 0.9611 & 0.9175 & 0.9383 & 0.8538 \\ NPV & 0.6752 & 0.6478 & 0.6687 & 0.7015 & 0.6581 \\ \hline \end{tabular}
\end{table}
Table 3: Summary of models’ performance results.  
**Author Contributions:** Conceptualization, [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]), [PERSON] ([PERSON]), H.A., L.L.; software, [PERSON] ([PERSON]), [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]), [PERSON] ([PERSON]), H.A., L.L.; formal analysis, [PERSON] ([PERSON]), [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]), [PERSON] ([PERSON]), H.A., L.L.; resources, [PERSON] ([PERSON]), L.L.; data curation, [PERSON] ([PERSON]), [PERSON] ([PERSON]), L.L.; writing--original draft preparation, [PERSON] ([PERSON]), [PERSON] ([PERSON]), H.A., L.L.; writing--review and editing, [PERSON] ([PERSON]), [PERSON] ([PERSON]), H.A., L.L.; visualization, J.C. ([PERSON]); supervision, J.C. ([PERSON]), H.A., L.L.; project administration, J.C. ([PERSON]), L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministerio de Ciencia e Innovacion Torres Quevedo grant number PTQ2021-012147.  
Not applicable.  
The authors declare no conflict of interest.