Predicting Post-Concussion Syndrome Outcomes with Machine Learning

Minhong Kim

Predicting Post-Concussion Syndrome Outcomes with Machine Learning

Quantitative Methods 2021-08-06 v1 Machine Learning

Authors: Minhong Kim

Abstract

In this paper, machine learning models are used to predict outcomes for patients with persistent post-concussion syndrome (PCS). Patients had sustained a concussion at an average of two to three months before the study. By utilizing assessed data, the machine learning models aimed to predict whether or not a patient would continue to have PCS after four to five months. The random forest classifier achieved the highest performance with an 85% accuracy and an area under the receiver operating characteristic curve (AUC) of 0.94. Factors found to be predictive of PCS outcome were Post-Traumatic Stress Disorder (PTSD), perceived injustice, self-rated prognosis, and symptom severity post-injury. The results of this study demonstrate that machine learning models can predict PCS outcomes with high accuracy. With further research, machine learning models may be implemented in healthcare settings to help patients with persistent PCS.

Keywords

clinical and biomedical research alzheimer's disease cognitive neuroscience

Cite

@article{arxiv.2108.02570,
  title  = {Predicting Post-Concussion Syndrome Outcomes with Machine Learning},
  author = {Minhong Kim},
  journal= {arXiv preprint arXiv:2108.02570},
  year   = {2021}
}

Related papers

View all related →