EconPapers    
Economics at your fingertips  
 

Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model

Alireza Tavakolian, Alireza Rezaee, Farshid Hajati () and Shahadat Uddin
Additional contact information
Alireza Tavakolian: Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran 14174, Iran
Alireza Rezaee: Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran 14174, Iran
Farshid Hajati: Intelligent Technology Innovation Laboratory (ITIL) Group, Institute for Sustainable Industries and Liveable Cities, Victoria University, Footscray, VIC 3011, Australia
Shahadat Uddin: School of Project Management, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia

Future Internet, 2023, vol. 15, issue 9, 1-21

Abstract: Hospital readmission and length-of-stay predictions provide information on how to manage hospital bed capacity and the number of required staff, especially during pandemics. We present a hybrid deep model called the Genetic Algorithm-Optimized Convolutional Neural Network (GAOCNN), with a unique preprocessing method to predict hospital readmission and the length of stay required for patients of various conditions. GAOCNN uses one-dimensional convolutional layers to predict hospital readmission and the length of stay. The parameters of the layers are optimized via 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 the 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 was 97.2% for diabetic patients. Furthermore, the accuracy of the length-of-stay prediction was 89%, 99.4%, and 94.1% for the 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 the healthcare funds and resources for patients with various diseases.

Keywords: readmission; length of stay; convolutional neural networks; genetic algorithm; diabetes; COVID-19 (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/15/9/304/pdf (application/pdf)
https://www.mdpi.com/1999-5903/15/9/304/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:15:y:2023:i:9:p:304-:d:1234088

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jftint:v:15:y:2023:i:9:p:304-:d:1234088