Abstract
In the United States alone, an estimated 1.7 million traumatic brain injuries (TBIs) occur each year, leading to more than 1.3 million TBI-related emergency room visits and hospitalizations, as well as thousands of deaths [9]. Following a mild TBI (mTBI) or concussion, patients may experience physical, psychological, and cognitive deficits lasting a period of hours, days, or for an extended period of time. Although most people recover within days to weeks of injury, some report persistent symptoms. Early detection, prognosis, and forecast of symptoms have the ability to improve the overall outcome of a patient, reduce the cost associated with treatment, and provide important insights into poorly understood brain injuries. This paper presents a deep learning approach for predicting the onset of a new diagnosis and its severity up to a year post concussion. Through the evaluation of our model we show that with thirty TBI-related symptoms, we are able to correctly predict the onset of a new symptom 93.49(±6.92)% of the time, and when predicting the entire 12-months trajectory of a patient’s symptoms we are able to exceed the expected 13.27% by more than doubling it to 33.53%. In addition, we introduce the concept of a deep recurrent neural network generating sample patients which can be used to derive the different types of patients that exist within the population.
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Dabek, F., Hoover, P., Caban, J. (2018). DeepDx: A Deep Learning Approach for Predicting the Likelihood and Severity of Symptoms Post Concussion. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_36
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DOI: https://doi.org/10.1007/978-3-030-05587-5_36
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