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

Cindy Crump; Christine Tsien Silvers; Bruce Wilson; Loretta Schlachta-Fairchild; Jeffrey Scott Ashley

doi:doi:10.11648/j.ajhr.20140206.17

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

Cindy Crump, Christine Tsien Silvers, Bruce Wilson, Loretta Schlachta-Fairchild, Jeffrey Scott Ashley

Published in American Journal of Health Research (Volume 2, Issue 6)

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.

Published in	American Journal of Health Research (Volume 2, Issue 6)
DOI	10.11648/j.ajhr.20140206.17
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), 2014. 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.
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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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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://article.sciencepublishinggroup.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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TY  - JOUR
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
AU  - Loretta Schlachta-Fairchild
AU  - Jeffrey Scott Ashley
Y1  - 2014/11/10
PY  - 2014
N1  - https://doi.org/10.11648/j.ajhr.20140206.17
DO  - 10.11648/j.ajhr.20140206.17
T2  - American Journal of Health Research
JF  - American Journal of Health Research
JO  - American Journal of Health Research
SP  - 361
EP  - 365
PB  - Science Publishing Group
SN  - 2330-8796
UR  - https://doi.org/10.11648/j.ajhr.20140206.17
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.
VL  - 2
IS  - 6
ER  -

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Author Information

Cindy Crump

AFrame Digital, Inc., Vienna, VA, USA
Christine Tsien Silvers

AFrame Digital, Inc., Vienna, VA, 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

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Plain Text BibTeX RIS

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
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
AU  - Loretta Schlachta-Fairchild
AU  - Jeffrey Scott Ashley
Y1  - 2014/11/10
PY  - 2014
N1  - https://doi.org/10.11648/j.ajhr.20140206.17
DO  - 10.11648/j.ajhr.20140206.17
T2  - American Journal of Health Research
JF  - American Journal of Health Research
JO  - American Journal of Health Research
SP  - 361
EP  - 365
PB  - Science Publishing Group
SN  - 2330-8796
UR  - https://doi.org/10.11648/j.ajhr.20140206.17
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.
VL  - 2
IS  - 6
ER  -

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