# Predicting Post-Concussion Syndrome Outcomes with Machine Learning

arXiv: https://arxiv.org/abs/2108.02570 · HTML: https://mlsanity.com/paper/2108.02570/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 15 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Built on by (most cited first, 1 of 1)

- [Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?](https://mlsanity.com/paper/2401.13045/) (2024)

## Abstract (arXiv)

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.