Volume 2, Issue 6, November 2014, Page: 361-365
Predicting Patient Outcomes Via Neural Network Estimation of Discharge APACHE Scores for Traumatic Brain Injury
Cindy Crump, AFrame Digital, Inc., Vienna, VA, USA; The Center for Study of Chronic Illness and Disability, George Mason University, Fairfax, VA, USA
Christine Tsien Silvers, AFrame Digital, Inc., Vienna, VA, USA; Children’s Hospital Informatics Program, Boston, MA, USA
Bruce Wilson, Barron Associates, Charlottesville, VA, USA
Loretta Schlachta-Fairchild, iTelehealth, Inc., Cocoa Beach, FL, USA
Jeffrey Scott Ashley, Center for Nursing Science and Clinical Inquiry, Walter Reed National Military Medical Center, Bethesda, MD, USA
Received: Oct. 21, 2014;       Accepted: Oct. 29, 2014;       Published: Nov. 10, 2014
DOI: 10.11648/j.ajhr.20140206.17      View  2700      Downloads  141
Abstract
It is highly desirable to be able to predict the likely outcome of critical patients admitted to the intensive care unit (ICU) for traumatic brain injury (TBI). Vital signs, laboratory values, and clinical assessments from throughout a patient’s ICU stay were collected retrospectively in an IRB-approved protocol from a Level I Trauma-Military Medical Center in the Southwest. ICU patients were included if they had been admitted for TBI during a five-year period ending in October 2007. Data were collected for 139 ICU patients with TBI. Admission and discharge APACHE IV scores were then derived from the collected data for each patient. A static back propagation neural network was developed to predict a patient’s ICU outcome vis-a-vis discharge APACHE IV scores. The resulting network, trained using leave-one-out methodology, was able to predict the discharge APACHE score on average within 12.9% of the actual score.
Keywords
APACHE Score, Intensive Care Unit, Neural Network, Patient Outcome, Prediction, Traumatic Brain Injury
To cite this article
Cindy Crump, Christine Tsien Silvers, Bruce Wilson, Loretta Schlachta-Fairchild, Jeffrey Scott Ashley, Predicting Patient Outcomes Via Neural Network Estimation of Discharge APACHE Scores for Traumatic Brain Injury, American Journal of Health Research. Vol. 2, No. 6, 2014, pp. 361-365. doi: 10.11648/j.ajhr.20140206.17
Reference
[1]
J.E. Zimmerman, A.A. Kramer, D.S. McNair, F.M. Malila,“Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients,” Crit Care Med, vol. 34, 2006, pp. 1297-1310.
[2]
M.A. de Jongh, M.H. Verhofstad, L.P. Leenen, “Accuracy of different survival prediction models in a trauma population,” Br J Surg., vol. 97, Epub 2010 Aug 19, pp. 1805-13.
[3]
S.C. Lim, A.C.K. Fok, Y.Y. Ong, “Patient outcome and intensive care resource allocation using APACHE II,” Singapore Med J., vol. 37, 1996, pp. 488-491.
[4]
F.H. Millham, W.W. LaMorte, “Factors associated with mortality in trauma: re-evaluation of the TRISS method using the National Trauma Data Bank,” J Trauma, vol. 56, 2004, pp. 1090-1096.
[5]
M.J. Vassar, F.R. Lewis Jr., J.A. Chambers, et al,“Prediction of outcome in intensive care unit trauma patients: a multicenter study of Acute Physiology and Chronic Health Evaluation (APACHE), Trauma and Injury Severity Score (TRISS), and a 24-hour intensive care unit (ICU) point system,” J Trauma, vol. 47, 1999, pp. 324-9.
[6]
S.M. DiRusso, T. Sullivan, C. Holly, et al,“An artificial neural network as a model for prediction of survival in trauma patients: validation for a regional trauma area,” J Trauma, vol. 49, 2000, pp. 212-223.
[7]
B. Eftekhar, K. Mohammad, H.E. Ardebili, et al,“Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data.” BMC Med Inform Decis Mak.,vol 5, 2005.
[8]
C.M. Ryan, D.A. Schoenfeld, W.P. Thorpe, et al,“Objective estimates of the probability of death from burn injuries,” N Engl J Med., vol. 338, 1998, pp. 362-6.
[9]
A. Knoll, M. De Kamps, “Roadmap of neuro-IT development," Ver 1.3: April 19, 2004, http://www.neuro-it.net/NeuroIT/Roadmap/RoadmapVersions/Roadmapv1.3.
[10]
M. Sordo, “Introduction to neural networks in healthcare,” Open Clinical, October, 2002.
[11]
ANNIMAB-1 Abstracts: Clinical diagnosis and medical decision support. www.phil.gu.se/ann/abstracts4.html.
[12]
R.W. Brause, “Medical analysis and diagnosis by neural networks, Lecture Notes in Computer Science, J.W. Goethe University, Frankfurt, Germany, 2001.
[13]
W. Ishaket al,“The potential of neural networks in medical applications,” July 2004, http://www.generation5.org/content/2004/NNAppMed.asp.
[14]
C. J. Langmead, “Generalized queries and Bayesian statistical model checking in dynamic Bayesian networks: application to personalized medicine,” Proceedings of The 8th Annual International Conference on Computational Systems Bioinformatics (CSB), 2009, pp. 201-212.
[15]
E. Castillo, J.M. Gutierrez, A.S. Hadi,“Learning Bayesian Networks,” in: Expert Systems and Probabilistic Network Models (Monographs in Computer Science). New York, NY: Springer-Verlag, 1997, pp. 481-528.
[16]
Z. Ghahramani. “Learning dynamic Bayesian networks,” Lecture Notes in Computer Science, vol. 1387, 1997, pp. 168-197.
[17]
F.V. Jensen, T.D. Nielsen, “Bayesian networks and decision graphs,” Information Science and Statistics series (2nd ed.). New York, NY; Springer Science + Business Media, LLC; 2007.
[18]
J. Pearl, S. Russell, “Bayesian networks,” In M.A. Arbib, Handbook of Brain Theory and Neural Networks. Cambridge, MA: MIT Press, 2002, pp. 157-160.
Browse journals by subject