Understanding the effects of concussion using big data

A concussion is a poorly understood mild traumatic brain injury (mTBI) that alters the way the brain functions. Clinical practice guidelines suggest different algorithms that should be followed to evaluate TBI patients [11]. At different steps, the clinical guidelines ask for physical, cognitive, behavioral, imaging, and neuropsychological evaluations which result in dozens of measures that should be analyzed to better establish an understanding of the patient's condition. The complexity, multimodal, and subjective properties of many of these data combined with the overlapping symptoms of comorbid issues raise many challenges for physicians who must integrate these disparate measurements to develop a comprehensive understanding of the patient's condition and for researchers trying to understand the effects of concussions. The complexity of determining the long-term effects of concussions as well as the data challenges faced by clinicians when trying to diagnose mTBI shows that research on the effects of concussion is a big data problem that only when large, comprehensive, and standardized clinical information are analyzed simultaneously, is when new knowledge can be obtained. This paper presents a large-scale informatics database that has been designed to enable research in the understanding of the effects of concussions. The database consists of millions of longitudinal clinical data points ranging from encounters, type of encounters, clinical notes, diagnosis codes, imaging findings, and other clinical information.