Using machine learning to predict concussion recovery time: The importance of psychological and symptomatic factors

The objectives were threefold: 1) To utilize machine learning (ML) to create a model for predicting concussion recovery time using routine clinical metrics, 2) To compare predictive factors within a ML model to previously identified risk factors, and 3) To compare predictive ability of ML models to traditional logistic regression.

North Texas Concussion Registry (ConTex) data were prospectively collected during an initial post-injury clinic visit and 3-month follow-up. ML models classified 1000 participants with sport- or recreation-related injuries, ages 6-59, into ordinal recovery time groups. Models were trained on an 80-20 train-test split with 5-fold cross validation. Performance was evaluated using area under the curve (AUC). Feature predictive importance was measured using Leave One Feature Out (LOFO) metrics and Permutation Feature Importance (PFI).

A CatBoost binary ML model classified participants into ≤14-d or >14-d recovery with an AUC of 0.79, similar to the logistic regression AUC of 0.77. In contrast, the multiclass model for recovery time had a lower AUC of 0.69. Time to clinic, symptom severity, and factors related to self-reported depressive symptoms, anxiety, and sleep quality had the largest feature importance values in the CatBoost model.

Post-injury depressive symptoms, anxiety, and sleep had a stronger influence in predicting prolonged recovery time than previously identified injury-related variables (e.g. loss of consciousness, headache). While promising, ML may not outperform traditional models depending on the simplicity and linearity of the predictor variables.