Healthc Inform Res.  2018 Apr;24(2):109-117. 10.4258/hir.2018.24.2.109.

Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System

Affiliations
  • 1Department of Health Services Management, School of Management and Medical Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran. ra_ravangard@yahoo.com
  • 2Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
  • 3Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
  • 4Shiraz Anesthesiology and Critical Care Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
  • 5Health Human Resources Research Centre, School of Management and Medical Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.

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.

Keyword

Forecasting; Neural Networks; Decision Support Techniques; Length of Stay; Heart Diseases; Cardiac Surgical Procedures; Intensive Care Unit

MeSH Terms

Cardiac Surgical Procedures
Critical Care*
Decision Support Techniques
Forecasting
Heart Diseases
Humans
Intensive Care Units*
Iran
Length of Stay*
Methods
Thoracic Surgery*

Figure

  • Figure 1 A part of the CART decision tree induced by 32 variables. In the decision tree, X = (x1,…, x32) are introduced as follows: x1 = age, x2 = gender, x3 = surgery type, x4 = hematocrit, x5 = type of operation, x6 = duration CPB, x7 = clamp time, x8 = LVEF, x10 = renal disease, x11 = reoperation, x13 = hypertension, x17 = OPCAB, x19 = CPB, x20 = sinus rhythm, x21 = myocardial infarction, x22 = mild valvulopathy, x24 = NYHA, x25 = creatinine, x27 = MIDCAB, x28 = HVS, x29 = hypercholesterolemia, x30 = preoperative infection, x32 = BMI. As an example inducted rule from decision tree: IF x8 < 3 AND x27 < 1.5 THEN length of stay = 11.2. CPB: cardiopulmonary bypass, LVEF: left ventricular ejection fraction, OPCAB: off-pump coronary artery bypass, NYHA: New York Heart Association, MIDCAB: minimally invasive direct coronary artery bypass, HVS: heart valve surgery, BMI: body mass index.


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