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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Alireza Tavakolian & Alireza Rezaee & Farshid Hajati & Shahadat Uddin, 2023. "Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model," Future Internet, MDPI, vol. 15(9), pages 1-21, September.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:9:p:304-:d:1234088
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    References listed on IDEAS

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