Tavakolian, A.; Rezaee, A.; Hajati, F.; Uddin, S. Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model. Future Internet2023, 15, 304.
Tavakolian, A.; Rezaee, A.; Hajati, F.; Uddin, S. Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model. Future Internet 2023, 15, 304.
Tavakolian, A.; Rezaee, A.; Hajati, F.; Uddin, S. Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model. Future Internet2023, 15, 304.
Tavakolian, A.; Rezaee, A.; Hajati, F.; Uddin, S. Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model. Future Internet 2023, 15, 304.
Abstract
Hospital readmission and length of stay prediction provide info to manage hospitals’ bed capacity and the number of required staff, especially during pandemics. We present a hybrid deep model called Genetic Algorithm-Optimized Convolutional Neural Network (GAOCNN) with a unique preprocessing method to predict hospital readmission and the length of stay in patients having various conditions. GAOCNN uses one-dimensional convolutional layers to predict hospital readmission and length of stay. The parameters of the layers are optimized using a genetic algorithm. To show the performance of the proposed model in patients with various conditions, we evaluate the model under three healthcare datasets; the Diabetes 130-US hospitals dataset, the COVID-19 dataset, and the MIMIC-III dataset. The diabetes 130-US hospitals dataset has information on both readmission and the length of stay, while COVID-19 and MIMIC-III datasets just include information on the length of stay. Experimental results show that the proposed model’s accuracy for hospital readmission is 97.2% for diabetic patients. Also, the accuracy of the length of stay prediction is 89%, 99.4%, and 94.1% for diabetic, COVID-19, and ICU patients, respectively. These results confirm the superiority of the proposed model compared to existing methods. Our findings offer a platform for managing healthcare funds and resources for patients with various diseases.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
