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A Neural Network Based Model for Predicting Psychological Conditions

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Brain Informatics and Health (BIH 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9250))

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Abstract

Preventive care attempts to inform individuals and clinicians of potential complications or conditions a patient might encounter. With the recent interest on leveraging big data in the healthcare domain to better design data-driven models for preventive medicine and the increased awareness of the long-lasting effects of concussions, being able to predict psychological conditions post concussion can have a paramount effect on mild traumatic brain injury patients. We present a neural network model that is able to predict the likelihood of developing psychological conditions such as anxiety, behavioral disorders, depression, and post-traumatic stress disorder. We analyzed the effectiveness of our model against a dataset of 89,840 patients. Our results show that we are able to achieve accuracies ranging from 73% to 95% for each of the clinical conditions under consideration, with an overall accuracy of 82.35% for all conditions.

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References

  1. Murdoch, T.B., Detsky, A.S.: THe inevitable application of big data to health care. JAMA 309(13), 1351–1352 (2013)

    Article  Google Scholar 

  2. Margolis, R., Derr, L., Dunn, M., Huerta, M., Larkin, J., Sheehan, J., Guyer, M., Green, E.D.: The National Institutes of Health’s Big Data to Knowledge (BD2k) initiative: capitalizing on biomedical big data. Journal of the American Medical Informatics Association : JAMIA 21(6), 957–958 (2014)

    Article  Google Scholar 

  3. O’Neil, M.E., Carlson, K., et al.: Definition of mTBI from the VA/DOD Clinical Practice Guideline for Management of Concussion/Mild Traumatic Brain Injury, U.S. Department of Veterans Affairs (2009)

    Google Scholar 

  4. Faul, M., Xu, L., Wald, M., Coronado, V.: CDC - TBI in the US Report - Traumatic Brain Injury - Injury Center, Centers for Disease Control and Prevention

    Google Scholar 

  5. DoD Worldwide Numbers for TBI. http://dvbic.dcoe.mil/dod-worldwide-numbers-tbi

  6. Bryant, R.A., Marosszeky, J.E., Crooks, J., Gurka, J.A.: Posttraumatic Stress Disorder After Severe Traumatic Brain Injury. American Journal of Psychiatry

    Google Scholar 

  7. Bryant, R.A., Nickerson, A., Creamer, M., et al.: Trajectory of post-traumatic stress following traumatic injury: 6-year follow-up. The British Journal of Psychiatry (2015). bjp.bp.114.145516

    Google Scholar 

  8. Hoge, C.W., Auchterlonie, J.L., Milliken, C.S.: Mental health problems, use of mental health services, and attrition from military service after returning from deployment to iraq or afghanistan. JAMA 295(9), 1023–1032 (2006)

    Article  Google Scholar 

  9. Anxiety and depression after mild head injury: a case control study 51

    Google Scholar 

  10. Kay, T.: Neuropsychological treatment of mild traumatic brain injury. The Journal of Head Trauma Rehabilitation 8 (3)

    Google Scholar 

  11. Caban, J., Riedy, G., Oakes, T., Grammer, G., DeGraba, T.: Understanding the effects of concussion using big data. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 18–23(2014). doi:10.1109/BigData.2014.7004387

  12. Hoogendoorn, M., Moons, L.M.G., Numans, M.E., Sips, R.-J.: Utilizing data mining for predictive modeling of colorectal cancer using electronic medical records. In: Ślzakȩ, D., Tan, A.-H., Peters, J.F., Schwabe, L. (eds.) BIH 2014. LNCS, vol. 8609, pp. 132–141. Springer, Heidelberg (2014)

    Google Scholar 

  13. Statnikov, A., Aliferis, C.F.: Are Random Forests Better than Support Vector Machines for Microarray-Based Cancer Classification? AMIA Annual Symposium Proceedings 2007, 686–690 (2007)

    Google Scholar 

  14. Yoo, I., Alafaireet, P., Marinov, M., et al.: Data Mining in Healthcare and Biomedicine: A Survey of the Literature. Journal of Medical Systems 36(4), 2431–2448 (2011)

    Article  Google Scholar 

  15. Karstoft, K.-I., Galatzer-Levy, I.R., Statnikov, A., Li, Z., Shalev, A.Y.: Bridging a translational gap: using machine learning to improve the prediction of PTSD. BMC Psychiatry 15 (1)

    Google Scholar 

  16. Dabek, F., Caban, J.: Leveraging big data to model the likelihood of developing psychological conditions after a concussion. In: 2015 INNS International Conference on Big Data (Big Data) (2015)

    Google Scholar 

  17. Rughani, A.I., Dumont, T.M., Lu, Z., et al.: Use of an artificial neural network to predict head injury outcome. Journal of Neurosurgery 113(3), 585–590 (2009)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. National Intrepid Center of Excellence (NICoE), Walter Reed National Military Medical Center, Bethesda, MD, USA

    Filip Dabek & Jesus J. Caban

Authors
  1. Filip Dabek
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  2. Jesus J. Caban
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Corresponding author

Correspondence to Filip Dabek .

Editor information

Editors and Affiliations

  1. Imperial College London , Data Science Institute, London, United Kingdom

    Yike Guo

  2. Institute of Cognitive Neuroscience, University College London, London, United Kingdom

    Karl Friston

  3. Imperial College London, London, United Kingdom

    Faisal Aldo

  4. Campus Biotech, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland

    Sean Hill

  5. Allen Institute for Brain Science, Seattle, USA

    Hanchuan Peng

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© 2015 Springer International Publishing Switzerland

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Dabek, F., Caban, J.J. (2015). A Neural Network Based Model for Predicting Psychological Conditions. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_25

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  • DOI: https://doi.org/10.1007/978-3-319-23344-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23343-7

  • Online ISBN: 978-3-319-23344-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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