Skip to main content
SpringerLink
Log in

Predicting Outcomes in Patients with Traumatic Brain Injury Using Machine Learning Models

  • Conference paper
  • First Online:
Intelligent Manufacturing and Mechatronics (SympoSIMM 2019)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Included in the following conference series:

Abstract

Traumatic brain injury (TBI) is defined as blunt and penetrating injury to the head and/or brain caused by an external force that leads to temporary or permanent impairments to the brain function. Accurate measurement of prediction for the outcomes of affected individual is highly desirable to plan and optimize treatment decision. The clinical experts predict the outcomes of brain injury patients with a high degree of accuracy based on their experience and the standardized Glasgow Outcome Scale (GOS). The GOS has been used over the past 40 years and it plays an important role in developing the understanding of brain injury. Recent developments in Artificial Intelligence (AI) have heightened the need for developing predictive models using machine learning (ML) methods especially for TBI patients who require life-saving interventions. ML is a subfield of AI which allows the computer algorithms to learn patterns by studying data directly without being explicitly programmed. This paper compares the different ways in which predictive models evaluate the potential of ML for TBI outcome prediction. A literature survey of latest articles from 2016 to 2018 reveals that the predictions of existing predictive models compute different prediction performances in terms of accuracy, sensitivity, specificity and area under receiving operator characteristic (ROC) curve (AUC). Depending on the specific prediction task evaluated and the type of input features included, Artificial Neural Network (ANN) creates a powerful model to predict outcomes of TBI with profound accuracy compared to other ML models. Although ANNs are considered as “black-box” in computational models, their benefits in clinical medicine have infinite potentials in evidence-based medicine practice because ANNs can be trained on new patient information. Moreover, the existing predictive models show that ML can be leveraged to more accurately predict the outcomes of TBI patients. Most importantly, predictive models can provide real-time clinical utilization that leads to greater accuracy and higher predictive value for patients suffered from traumatic brain injury.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Knaus, W.A., Draper, E.A., Wagner, D.P., Zimmerman, J.E.: APACHE II: a severity of disease classification system. Crit. Care Med. 13, 818–829 (1985). https://doi.org/10.1097/00003246-198510000-00009

    Article  Google Scholar 

  2. MRC CRASH Trial Collaborators: Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ 336(7641), 425–429 (2008). https://doi.org/10.1136/bmj.39461.643438.25

    Article  Google Scholar 

  3. Rimel, R.W., Jane, J.A., Edlich, R.F.: An injury severity scale for comprehensive management of central nervous system trauma. J. Am. Coll. Emerg. Physicians 8, 64–67 (1979). https://doi.org/10.1016/S0361-1124(79)80039-8

    Article  Google Scholar 

  4. Mitra, J., Shen, K.K., Ghose, S., Bourgeat, P., Fripp, J., Salvado, O., Pannek, K., Taylor, D.J., Mathias, J.L., Rose, S.: Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks. NeuroImage 129, 247–259 (2016). https://doi.org/10.1016/j.neuroimage.2016.01.056

    Article  Google Scholar 

  5. Alanazi, H.O., Abdullah, A.H., Al Juma, M.: A critical review for an accurate and dynamic prediction for the outcomes of traumatic brain injury based on Glasgow Outcome Scale. J. Med. Sci. 13(4), 244–252 (2013). https://doi.org/10.3923/jms.2013.244.252

    Article  Google Scholar 

  6. Alanazi, H.O., Abdullah, A.H., Qureshi, K.N., Larbani, M., Al Jumah, M.: Predicting the outcomes of traumatic brain injury using accurate and dynamic predictive model. J. Theor. Appl. Inf. Technol. 93(2), 561–570 (2016)

    Google Scholar 

  7. Alanazi, H.O., Abdullah, A.H., Qureshi, K.N.: A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care. J. Med. Syst. 41(4), 69 (2017). https://doi.org/10.1007/s10916-017-0715-6. (in Eng)

    Article  Google Scholar 

  8. Moppett, I.K.: Traumatic brain injury: assessment, resuscitation and early management. Br. J. Anaesth. 99(1), 18–31 (2007). https://doi.org/10.1093/bja/aem128

    Article  Google Scholar 

  9. You, X., Liew, B.S., Rosman, A.K., Musa, K.I., Idris, Z.: The estimated cost of surgically managed isolated traumatic head injury secondary to road traffic accidents. Neurosurg. Focus 44(5), E7 (2018). https://doi.org/10.3171/2018.1.FOCUS17796

    Article  Google Scholar 

  10. Goffus, A.M., Anderson, G.D., Hoane, M.R.: Sustained delivery of nicotinamide limits cortical injury and improves functional recovery following traumatic brain injury. J. Oxidative Med. Cell. Longev. 3(2), 145–152 (2010). https://doi.org/10.4161/oxim.3.2.11315

    Article  Google Scholar 

  11. Emami, P., Czorlich, P., Fritzsche, F.S., Westphal, M., Rueger, J.M., Lefering, R., Hoffmann, M.: Impact of Glasgow Coma Scale score and pupil parameters on mortality rate and outcome in pediatric and adult severe traumatic brain injury: a retrospective, multicenter cohort study. J. Neurosurg. 126(3), 760–767 (2017). https://doi.org/10.3171/2016.1.JNS152385

    Article  Google Scholar 

  12. McMillan, T., Wilson, L., Ponsford, J., Levin, H., Teasdale, G., Bond, M.: The Glasgow Outcome Scale—40 years of application and refinement. Nat. Rev. Neurol. 12(8), 477 (2016). https://doi.org/10.1038/nrneurol.2016.89

    Article  Google Scholar 

  13. Jennett, B., Bond, M.: Assessment of outcome after severe brain damage: a practical scale. Lancet 305(7905), 480–484 (1975). https://doi.org/10.1016/S0140-6736(75)92830-5

    Article  Google Scholar 

  14. Hale, A.T., Stonko, D.P., Brown, A., Lim, J., Voce, D.J., Gannon, S.R., Le, T.M., Shannon, C.N.: Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury. Neurosurg. Focus 45(November), 1–7 (2018). https://doi.org/10.3171/2018.8.FOCUS17773

    Article  Google Scholar 

  15. Senders, J.T., Arnaout, O., Karhade, A.V., Dasenbrock, H.H., Gormley, W.B., Broekman, M.L., Smith, T.R.: Natural and artificial intelligence in neurosurgery: a systematic review. Neurosurgery 83(2), 181–192 (2017). https://doi.org/10.1093/neuros/nyx384

    Article  Google Scholar 

  16. Senders, J.T., Staples, P.C., Karhade, A.V., Zaki, M.M., Gormley, W.B., Broekman, M.L.D., Smith, T.R., Arnaout, O.: Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg. 109(Ml), 476.e471–486.e471 (2018). https://doi.org/10.1016/j.wneu.2017.09.149

    Article  Google Scholar 

  17. Senders, J.T., Zaki, M.M., Karhade, A.V., Chang, B., Gormley, W.B., Broekman, M.L., Smith, T.R., Arnaout, O.: An introduction and overview of machine learning in neurosurgical care. Acta Neurochir. 160(1), 29–38 (2018). https://doi.org/10.1007/s00701-017-3385-8

    Article  Google Scholar 

  18. Lu, H.-Y., Li, T.-C., Tu, Y.-K., Tsai, J.-C., Lai, H.-S., Kuo, L.-T.: Predicting long-term outcome after traumatic brain injury using repeated measurements of Glasgow Coma Scale and data mining methods. J. Med. Syst. 39(2), 14 (2015). https://doi.org/10.1007/s10916-014-0187-x

    Article  Google Scholar 

  19. Gholipour, C., Rahim, F., Fakhree, A., Ziapour, B.: Using an artificial neural networks (ANNs) model for prediction of intensive care unit (ICU) outcome and length of stay at hospital in traumatic patients. J. Clin. Diagn. Res. 9(4), 19–23 (2015). https://doi.org/10.7860/JCDR/2015/9467.5828

    Article  Google Scholar 

  20. Liu, N.T., Salinas, J.: Machine learning for predicting outcomes in trauma. Shock 48(5), 504–510 (2017). https://doi.org/10.1097/SHK.0000000000000898. (in Eng)

    Article  Google Scholar 

  21. Kamal, H., Lopez, V., Sheth, S.A.: Machine learning in acute ischemic stroke neuroimaging. Front. Neurol. 9(7–12), 2018 (2018). https://doi.org/10.3389/fneur.2018.00945

    Article  Google Scholar 

  22. Kotsiantis, S.B., Zaharakis, I.D., Pintelas, P.E.: Machine learning: a review of classification and combining techniques. Artif. Intell. Rev. 26(3), 159–190 (2006). https://doi.org/10.1007/s10462-007-9052-3

    Article  Google Scholar 

  23. Juhola, M., Laurikkala, J.: Missing values: how many can they be to preserve classification reliability? Artif. Intell. Rev. 40(3), 231–245 (2013). https://doi.org/10.1007/s10462-011-9282-2

    Article  Google Scholar 

  24. Saar-Tsechansky, M., Provost, F.: Handling missing values when applying classification models. J. Mach. Learn. Res. 8, 1623–1657 (2007)

    MATH  Google Scholar 

  25. McGonigal, M.D., Cole, J., Schwab, C.W., Kauder, D.R., Rotondo, M.F., Angood, P.B.: A new approach to probability of survival scoring for trauma quality assurance. J. Trauma 34(6), 863–868 (1993)

    Article  Google Scholar 

  26. Pourahmad, S., Hafizi-Rastani, I., Khalili, H., Paydar, S.: Identifying important attributes for prognostic prediction in traumatic brain injury patients. Methods Inf. Med. 55(05), 440–449 (2016). https://doi.org/10.3414/ME15-01-0080

    Article  Google Scholar 

  27. Shafiei, E., Fakharian, E., Omidi, A., Akbari, H., Delpisheh, A., Nademi, A.: Comparison of artificial neural network and logistic regression models for prediction of psychological symptom six months after mild traumatic brain injury. Iran. J. Psychiatry Behav. Sci. 11(3), e5849 (2017). https://doi.org/10.17795/ijpbs-5849

    Article  Google Scholar 

  28. Alanazi, H.O., Abdullah, A.H., Qureshi, K.N., Ismail, A.S.: Accurate and dynamic predictive model for better prediction in medicine and healthcare. Ir. J. Med. Sci. 128(2), 1–13 (2018). https://doi.org/10.1007/s11845-017-1655-3

    Article  Google Scholar 

  29. Hale, A.T., Stonko, D.P., Lim, J., Guillamondegui, O.D., Shannon, C.N., Patel, M.B.: Using an artificial neural network to predict traumatic brain injury. J. Neurosurg. Pediatr. 1, 1–8 (2018). https://doi.org/10.3171/2018.8.PEDS18370

    Article  Google Scholar 

  30. Rau, C.-S., Kuo, P.-J., Chien, P.-C., Huang, C.-Y., Hsieh, H.-Y., Hsieh, C.-H.: Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models. PLoS ONE 13(11), 1–12 (2018). https://doi.org/10.1371/journal.pone.0207192

    Article  Google Scholar 

  31. Kabir, G., Ahsan Akhtar Hasin, M.: Comparative analysis of artificial neural networks and neuro-fuzzy models for multicriteria demand forecasting. Int. J. Fuzzy Syst. Appl.: IJFSA 3, 1–24 (2013). https://doi.org/10.4018/ijfsa.2013010101

    Article  Google Scholar 

  32. Agoston, D.V., Langford, D.: Big data in traumatic brain injury: promise and challenges. Concussion 2, 45 (2017). https://doi.org/10.2217/cnc-2016-0013

    Article  Google Scholar 

  33. Johannesen, J.K., Bi, J., Jiang, R., Kenney, J.G., Chen, C.-M.A.: Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults. Neuropsychiatr. Electrophysiol. 2(1), 3 (2016). https://doi.org/10.1186/s40810-016-0017-0

    Article  Google Scholar 

Download references

Acknowledgement

This research is supported by the Ministry of Higher Education (MoHE) Malaysia, under Trans-disciplinary Research Grant Scheme (TRGS) with grant number 203\PELECT\6768002.

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia

    Nor Safira Elaina Mohd Noor & Haidi Ibrahim

Authors
  1. Nor Safira Elaina Mohd Noor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haidi Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haidi Ibrahim .

Editor information

Editors and Affiliations

  1. Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Zamberi Jamaludin

  2. Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Mohd Najib Ali Mokhtar

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mohd Noor, N.S.E., Ibrahim, H. (2020). Predicting Outcomes in Patients with Traumatic Brain Injury Using Machine Learning Models. In: Jamaludin, Z., Ali Mokhtar, M.N. (eds) Intelligent Manufacturing and Mechatronics. SympoSIMM 2019. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-9539-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9539-0_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9538-3

  • Online ISBN: 978-981-13-9539-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation