Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System / 대한의료정보학회지
Healthcare Informatics Research
;
: 109-117, 2018.
Article
in English
| WPRIM
| ID: wpr-714033
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Thoracic Surgery
/
Decision Support Techniques
/
Critical Care
/
Forecasting
/
Heart Diseases
/
Cardiac Surgical Procedures
/
Intensive Care Units
/
Iran
/
Length of Stay
/
Methods
Type of study:
Prognostic study
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
Healthcare Informatics Research
Year:
2018
Type:
Article
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