TBI2Vec: Traumatic Brain Injury Smartphone Sensing using AutoEncoder Embeddings

TBI causes distress to millions of individuals and can lead to significant motor, cognitive and emotional deficits. However, TBI patients are currently assessed infrequently especially in-between scheduled appointments. To facilitate passive, remote, population-level ailment monitoring, we propose TBI2Vec, a TBI sensing framework that continuously assesses smartphone users by using machine learning to classify smart-phone sensor features encoded as autoencoder embeddings. Passive smartphone sensing of TBI enables a small medical team to monitor a large population of patients passively and detect TBI early. In analyzing a large, real, crowd-sourced smartphone sensor TBI dataset, we extracted 106 statistical features from raw smartphone sensor data, from which we generated autoencoder embeddings that are then classified to distinguish TBI subjects from non-TBI subjects. In rigorous evaluations, we found that classifying the features encoded using embeddings outperformed the same models without embeddings. The dimension reduction process of generating autoencoder embeddings retained the most discriminative in-formation while eliminating non-discriminative ones, boosting classification both accuracy and generalizability. Even for our imbalanced dataset, using embeddings performed better than baseline models in classifying the minority class. In rigorous evaluation, using embeddings increased the F-beta(0.5) by 34-71%, decreased the False Negative Rate (FNR) by 20-100% and significantly reduced the cross-fold variation of accuracies achieved during k-fold cross validation. TBI2Vec was able to detect TBI occurrence as early as 24 hours after injury. Random Forest performed the best and a window size of 2 days 12 hours gave the best results. TBI2Vec achieved an F-beta (0.5) score of 83.0% with a True Negative Rate (TNR) of 96.0% and False Negative Rate of 33.0% by correctly identifying TBI subjects.