Abstract
Weaning patients efficiently off mechanical ventilation continues to be a challenge for clinical professionals. Medical professionals usually make a decision to wean based mostly on their own experience. In this study, we present a weaning success rate prediction system (WSRPS); it is a computer-aided system developed from MatLab interfaces using Artificial Neural Network (ANN) algorithms. The goal of this system is to help doctors objectively and effectively predict whether weaning is appropriate for patients based on the patients’ clinical data. The system can also output Microsoft EXCEL (XLS) files (i.e. a database including the basic data of patients and doctors) to medical officers. To our knowledge, this is the first design approach of its kind to be used in the study of weaning success rate prediction.
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References
Battiti, R.: First and second order methods for learning: Between steepest descent and Newton’s method. Neural Computation, 141–166 (1992)
Baxt, W.G.: Use of an artificial neural network for the diagnosis of myocardial infarction. Ann. Intern. Med. 115(11), 843–848 (1991)
Burns, S.M., et al.: Design, testing and results of an outcomes managed approach to patients requiring prolonged ventilation. Am. J. Crit Care 7, 45–57 (1998)
Burns, S.M., et al.: The weaning continuum use of Acute Physiology and Chronic Health Evaluation III, Burns Wean Assessment Program, Therapeutic Intervention Scoring System, and Wean Index scores to establish stages of weaning. Critical Care Med. 2259–2267 (2000)
Burns, S.M., et al.: Implementation of an institutional program to improve clinical and financial outcomes of mechanically ventilated patients: One-year outcomes and lessons learned. Critical Care Med., 2752–2763 (2003)
DiRusso, S.M., et al.: An Artificial Neural Network as a Model for Prediction of Survival in Trauma Patients: Validation for a Regional Trauma Area. J. Trauma 49, 212–223 (2000)
Dorsey, S.G., et al.: A neural network model for predicting pancreas transplant graft outcome. Diabetes Care 20(7), 1128–1133 (1997)
Esteban, A., et al.: A comparison of four methods of weaning patients from mechanical ventilation. NEJM 332, 345–350 (1995)
Esteban, A., et al.: Characteristics and Outcomes in Adult Patients Receiving Mechanical Ventilation. JAMA 287(3), 345–355 (2002)
Kaohsiung Veterans General Hospital: Shallow talking about respirator, http://www.vghks.gov.tw/cm/html/health/symptom/EduMV.htm
Kemeny, V., et al.: Automatic embolus detection by a neural network. Stroke 30, 807–810 (1999)
Kurek, C.J., et al.: Clinical and economic outcomes of mechanically ventilated patients in New York State during 1993. Chest 114, 214–222 (1998)
Li, Y.C., et al.: Neural network modeling for surgical decisions on traumatic brain injury patients. Int. J. Med. Inf. 57(1), 1–9 (2000)
Marquez, G., et al.: Development of tissue-simulating optical phantoms: poly-N-isopropylacrylamide solution entrapped inside a hydrogel. Phys. Med. Biol., 309–318 (1999)
Moller, M.F.: A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. Neural networks, 525–533 (1993)
Nguyen, T., et al.: Comparison of prediction models for adverse outcome in pediatric meningococcal disease using artificial neural network and logistic regression analyses. Journal of Clinical Epidemiolog 55, 687–695 (2002)
Rosen, R.L., Bone, R.C.: Financial implications of ventilator care. Crit Care Clin. 6, 797–805 (1990)
Vijaya, G., et al.: ANN-based QRS-complex analysis of ECG. J. Med. Eng. Technol. 22, 160–167 (1998)
Yang, K.L., Tobin, M.J.: A prospective study of indexes predicting the outcome of trials of weaning from mechanical ventilation. NEJM 324, 1445–1450 (1991)
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Chen, A.H., Chen, G.T. (2009). WSRPS: A Weaning Success Rate Prediction System Based on Artificial Neural Network Algorithms. In: Koutsojannis, C., Sirmakessis, S. (eds) Tools and Applications with Artificial Intelligence. Studies in Computational Intelligence, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88069-1_8
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DOI: https://doi.org/10.1007/978-3-540-88069-1_8
Publisher Name: Springer, Berlin, Heidelberg
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