Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System.
10.4258/hir.2018.24.2.109
- Author:
Hamidreza MAHARLOU
1
;
Sharareh R NIAKAN KALHORI
;
Shahrbanoo SHAHBAZI
;
Ramin RAVANGARD
Author Information
1. Department of Health Services Management, School of Management and Medical Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran. ra_ravangard@yahoo.com
- Publication Type:Original Article
- Keywords:
Forecasting;
Neural Networks;
Decision Support Techniques;
Length of Stay;
Heart Diseases;
Cardiac Surgical Procedures;
Intensive Care Unit
- MeSH:
Cardiac Surgical Procedures;
Critical Care*;
Decision Support Techniques;
Forecasting;
Heart Diseases;
Humans;
Intensive Care Units*;
Iran;
Length of Stay*;
Methods;
Thoracic Surgery*
- From:Healthcare Informatics Research
2018;24(2):109-117
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. METHODS: A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. RESULTS: The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). CONCLUSIONS: The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.
