https://doi.org/10.22037/jcma.v6i2.32395

Predicting Patient Length of Stay in the Neurosurgical Intensive Care Unit of an Academic Medical Center: Predicting Patient Length of Stay in a Neurosurgical Intensive Care Unit

authors:

avatar Behrooz Alizadeh Savareh , * , avatar Ahmad Alibabaei , avatar Soleiman Ahmady ORCID 1 , avatar Majid Mokhtari ORCID , avatar Mohammadreza Hajiesmaeili ORCID 2 , avatar Saeedeh Nateghinia 2

Virtual School of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Anesthesiology and Critical Care Department, Critical Care Quality Improvement Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Journal of Cellular & Molecular Anesthesia: Vol.6, issue 2; e149658
published online: December 15, 2020
article type: Original Articles

How To Cite Alizadeh Savareh B, Alibabaei A, Ahmady S, Mokhtari M, Hajiesmaeili M, et al. Predicting Patient Length of Stay in the Neurosurgical Intensive Care Unit of an Academic Medical Center: Predicting Patient Length of Stay in a Neurosurgical Intensive Care Unit. J Cell Mol Anesth. 2021;6(2):e149658. https://doi.org/10.22037/jcma.v6i2.32395.

Abstract

Background: The intensive care unit (ICU) has the highest mortality and admission rates compared to other wards. Therefore, to increase the performance of hospital services, it is very important to evaluate indicators such as mortality and length of stay of patients in ICU. The present study aimed to investigate the neural network analysis method and Particle Swarm Optimization - Support Vector Machine to predict the length of stay in the neurosurgical intensive care unit. Materials and Methods: This descriptive research deals with data mining and modeling of intensive care unit processes, leading to a practical example of the application of health systems engineering knowledge, using MATLAB software. Data of 1200 patients admitted during the years 2017 to 2019 in the intensive care unit of neurosurgery. Then we evaluated all data with SVM + PSA and NCA. Results: Identifying the important features and using them has gradually reduced the LOS prediction error from 40% to 7%. Using the NCA technique makes better results for predicting ICU LOS. Conclusion: PSO + SMV in addition to NCA is a good predictor of ICU LOS screening in patients after neurosurgery and can provide more accurate prognostic factors.

References

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    References are in the PDF file of the article.