AI-Driven Prognosis of Traumatic Brain Injury

Abstract

Traumatic brain injury (TBI) is a critical health issue, requiring an accurate and timely prognosis to inform medical interventions. This paper focuses on machine learning techniques to predict patients\' outcomes, specifically targeting the Indian demographic. Key metrics such as age, time since injury, Glasgow Coma Scale (GCS), and Glasgow Outcome Scale (GOS) are utilized to assess damage severity and forecast mortality and morbidity. By integrating a Random Forest Classifier for mortality prediction and a Random Forest Regressor for morbidity estimation, the system provides a required outcome according to the input of the patient\'s condition. The models are trained on simulated data and integrated into a web-based platform, enabling automated predictions and user-friendly interfaces for healthcare providers. This approach gives the diagnostic accuracy, supports critical decision-making, and promotes timely medical responses, ultimately improving patient care outcomes.

Introduction

1. Overview

TBI is a major health concern globally and especially in India due to rising road accidents. Accurate and timely prognosis is essential but challenging in Indian healthcare due to manual assessments and lack of localized tools. This study introduces a machine learning (ML)-based decision support system tailored for Indian clinical settings.

2. Objectives

• Automate TBI prognosis using Random Forest models.

• Predict:

  • Mortality (classification: survived or not)

  • Morbidity (regression: predicted GOS score)

• Create a web-based application for real-time clinical decision support.

• Use commonly available clinical parameters: age, time since injury, GCS, GOS.

3. Literature Foundation

• Prior studies emphasized standardized data elements like GCS/GOS.

• ML models like Random Forest have proven effective in mortality prediction.

• Research supports regional model tuning due to demographic differences.

• Indian-specific challenges include data scarcity and infrastructural limitations.

• Similar platforms have shown success but lacked Indian data focus.

4. Problem Definition

• TBI prognosis in India is inconsistent and time-consuming due to:

  • Manual assessments

  • Lack of standardized Indian tools

  • Inadequate predictive support systems

• There's a need for an intelligent, localized, and accessible system to support clinicians.

5. Methodology

Data Handling:

• Simulated dataset created based on published clinical distributions.

Modeling:

• Random Forest Classifier → Predict mortality.

• Random Forest Regressor → Predict morbidity (GOS score).

• Metrics used: Accuracy, Recall, F1-score (classification); MSE, R² (regression).

System Integration:

• Web interface allows clinicians to input data and receive instant outcome predictions.

• Backend invokes trained models for real-time assessment.

6. Results

• Mortality Prediction Accuracy: 85%

  • Recall: 0.82

• Morbidity Prediction (GOS):

  • MSE: 0.15

• Web Platform:

  • Simple, responsive, and easy to use.

  • Enables faster and more accurate clinical decisions.

7. Limitations and Future Work

• Real-world validation needed with actual patient data.

• Future enhancements could include:

  • Neuroimaging inputs

  • Broader clinical indicators

  • Real-time data integration from hospital systems

Conclusion

This project develops a machine learning-based system to predict the outcomes of Traumatic Brain Injury (TBI) patients, specifically tailored to the Indian demographic. By leveraging clinical indicators such as age, time since injury, Glasgow Coma Scale (GCS), and Glasgow Outcome Scale (GOS), the system uses a Random Forest Classifier for mortality prediction and a Random Forest Regressor for morbidity estimation. The models, trained on simulated data, are integrated into a user-friendly web-based platform that provides real-time, data-driven predictions to healthcare professionals, enhancing diagnostic accuracy and supporting faster clinical decision-making.The proposed system addresses the lack of region-specific predictive tools for TBI prognosis, particularly in resource-constrained settings like India. It provides healthcare providers with a reliable, automated tool that reduces dependency on subjective judgment and helps make timely decisions that can significantly improve patient outcomes. This project highlights the potential of machine learning to transform clinical practices, and future work can enhance the system by incorporating real-world data, expanding predictive features, and integrating additional metrics like neuroimaging data. Ultimately, it contributes to the growing intersection of AI and healthcare, with the potential to improve both patient care and the efficiency of trauma management.

References

[1] Saatman, K. E., Duhaime, A.-C., Bullock, R., Maas, A. I. R., Valadka, A., & Manley, G. T. (2008). Classification of traumatic brain injury for targeted therapies. Journal of Neurotrauma, 25(7), 719–738. https://doi.org/10.1089/neu.2008.0586 [2] Raj, R., Luostarinen, T., Skrifvars, M. B., Bendel, S., Reinikainen, M., &Kortelainen, J. (2019). Machine learning-based dynamic mortality prediction after traumatic brain injury. Scientific Reports, 9, 17672. https://doi.org/10.1038/s41598-019-53745-5 [3] Yue, J. K., Vassar, M. J., Lingsma, H. F., Cooper, S. R., Okonkwo, D. O., & Maas, A. I. R. et al. (2013). Transforming research and clinical knowledge in traumatic brain injury pilot: multicenter implementation of the common data elements for traumatic brain injury. Journal of Neurotrauma, 30(22), 1831–1844. https://doi.org/10.1089/neu.2013.2970 [4] Badhiwala, J. H., Wilson, J. R., Schroeder, G. D., Singh, A., &Fehlings, M. G. (2020). The influence of demographic and clinical characteristics on the prognostic performance of TBI outcome models: a systematic review and meta-analysis. The Lancet Neurology, 19(2), 162–172. https://doi.org/10.1016/S1474-4422(19)30394-7 [5] Irimia, A., Chambers, M. C., Torgerson, C. M., & Van Horn, J. D. (2012). Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction. NeuroImage: Clinical, 1(1), 1–17. https://doi.org/10.1016/j.nicl.2012.08.002 [6] Gourav, K., Srivastava, A., & Bhatia, M. P. S. (2020). Applications of artificial intelligence and machine learning in traumatic brain injury: Indian healthcare perspective. International Journal of Medical Informatics, 141, 104245. https://doi.org/10.1016/j.ijmedinf.2020.104245 [7] Han, S. Y., Jung, H. W., Han, C. Y., &Jeong, H. T. (2020). Comparative performance of machine learning models for traumatic brain injury prognosis. Computers in Biology and Medicine, 123, 103908. https://doi.org/10.1016/j.compbiomed.2020.103908 [8] Khetan, A., Sharma, R., & Mehta, R. (2022). Development of a web-based clinical decision support system for traumatic brain injury using predictive modeling. Health Informatics Journal, 28(1), 14604582221075432. https://doi.org/10.1177/14604582221075432 [9] Jha, R. M., Puccio, A. M., Chandra, A., Okonkwo, D. O., & Park, Y. (2018). Comparative analysis of machine learning models for prediction of outcomes in traumatic brain injury. Journal of Neurotrauma, 35(6), 745–755. https://doi.org/10.1089/neu.2017.5102 [10] World Health Organization. (2021). Traumatic Brain Injury in South-East Asia: WHO Regional Report. World Health Organization, Regional Office for South-East Asia. https://www.who.int/southeastasia

Copyright

Copyright © 2025 E Bharath, Goutham K, Austin Issac, Abdul Musawwirs. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.