A Deep Learning Approach for Predicting Hospital Length of Stay for People with Diabetes using Electronic Health Records (EHRs)

Abstract

Diabetes, which substantially affects the people lives as well as the healthcare systems worldwide, has become the primary area of concern. The purpose of this study is to predict the length of hospital stay among diabetic patients using machine learning (ML), deep learning (DL), along with electronic health record (EHR) data. This work evaluated several models: XGBoost, Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network with LSTM (RNN-LSTM), and an ensemble model combining GAT and CNN in terms of performance metrics used, such as MAE, MSE, and R². Among all the models tested, MLP using the top 20 features performed the best, recording an MAE of 0.37 and an R² of 0.62. The model emphasized certain critical aspects concerning feature selection and dimensionality reduction, which enhanced the algorithm's accuracy, especially in cases with numerous redundant or verbose variables. More generally, these findings point to the potential that predictive modeling and artificial intelligence have in optimizing the allocation of hospital resources, reducing the costs of health care, and improving patient outcomes, thereby paving the way forward in the development of some clinical decision support tools to manage inpatient diabetic care.