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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2018.1
- 수록면
- 109 - 117 (9page)
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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.
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참고문헌 (38)
참고문헌 신청
Noorizad S / 2005 / Causes of mortality and morbidity in a neurosurgery ICU in Kashan 1999-2001 / FEYZ 9(2):15~20
University of California San Francisco / 2018 / ICU outcome [Internet]
Verburg IW / 2014 / Comparison of regression methods for modeling intensive care length of stay / PLoS One 9(10):e109684
Ghoreishi Nejad S / 2008 / Agent simulation in healthcare
Widyastuti Y / 2012 / Length of intensive care unit stay following cardiac surgery: is it impossible to find a universal prediction model? / Interact Cardiovasc Thorac Surg 15(5):825~832
University of California San Francisco / 2018 / ICU outcome [Internet]
Verburg IW / 2014 / Comparison of regression methods for modeling intensive care length of stay / PLoS One 9(10):e109684
Ghoreishi Nejad S / 2008 / Agent simulation in healthcare
Widyastuti Y / 2012 / Length of intensive care unit stay following cardiac surgery: is it impossible to find a universal prediction model? / Interact Cardiovasc Thorac Surg 15(5):825~832
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