American Journal of Health Research

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Predicting Patient Outcomes Via Neural Network Estimation of Discharge APACHE Scores for Traumatic Brain Injury

Received: 21 October 2014    Accepted: 29 October 2014    Published: 10 November 2014
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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.

DOI 10.11648/j.ajhr.20140206.17
Published in American Journal of Health Research (Volume 2, Issue 6, November 2014)
Page(s) 361-365
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

APACHE Score, Intensive Care Unit, Neural Network, Patient Outcome, Prediction, Traumatic Brain Injury

References
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[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.
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[10] M. Sordo, “Introduction to neural networks in healthcare,” Open Clinical, October, 2002.
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[13] W. Ishaket al,“The potential of neural networks in medical applications,” July 2004, http://www.generation5.org/content/2004/NNAppMed.asp.
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[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.
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Author Information
  • AFrame Digital, Inc., Vienna, VA, USA; The Center for Study of Chronic Illness and Disability, George Mason University, Fairfax, VA, USA

  • AFrame Digital, Inc., Vienna, VA, USA; Children’s Hospital Informatics Program, Boston, MA, USA

  • Barron Associates, Charlottesville, VA, USA

  • iTelehealth, Inc., Cocoa Beach, FL, USA

  • Center for Nursing Science and Clinical Inquiry, Walter Reed National Military Medical Center, Bethesda, MD, USA

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  • APA Style

    Cindy Crump, Christine Tsien Silvers, Bruce Wilson, Loretta Schlachta-Fairchild, Jeffrey Scott Ashley. (2014). Predicting Patient Outcomes Via Neural Network Estimation of Discharge APACHE Scores for Traumatic Brain Injury. American Journal of Health Research, 2(6), 361-365. https://doi.org/10.11648/j.ajhr.20140206.17

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    ACS Style

    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. Am. J. Health Res. 2014, 2(6), 361-365. doi: 10.11648/j.ajhr.20140206.17

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    AMA Style

    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. Am J Health Res. 2014;2(6):361-365. doi: 10.11648/j.ajhr.20140206.17

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  • @article{10.11648/j.ajhr.20140206.17,
      author = {Cindy Crump and Christine Tsien Silvers and Bruce Wilson and Loretta Schlachta-Fairchild and Jeffrey Scott Ashley},
      title = {Predicting Patient Outcomes Via Neural Network Estimation of Discharge APACHE Scores for Traumatic Brain Injury},
      journal = {American Journal of Health Research},
      volume = {2},
      number = {6},
      pages = {361-365},
      doi = {10.11648/j.ajhr.20140206.17},
      url = {https://doi.org/10.11648/j.ajhr.20140206.17},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajhr.20140206.17},
      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.},
     year = {2014}
    }
    

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    T1  - Predicting Patient Outcomes Via Neural Network Estimation of Discharge APACHE Scores for Traumatic Brain Injury
    AU  - Cindy Crump
    AU  - Christine Tsien Silvers
    AU  - Bruce Wilson
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    AU  - Jeffrey Scott Ashley
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    AB  - 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.
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