Big data, artificial intelligence, and machine learning in neurotrauma

Rapid advances in the collection, storage, and analysis of large volumes of data-Big Data-offer the much-needed help to identify and treat the various pathological conditions triggered by traumatic brain injury (TBI). Big Data (BD) is defined as extremely large, complex, and mostly unstructured data that cannot be analyzed using traditional approaches. BD can be only analyzed by using text mining (TM), artificial intelligence (AI), or machine learning (ML). These approaches can reveal patterns, trends, and associations, critical for understanding the “most complex disease of the most complex organ.” While powerful and successfully tested computational tools are available, using BD approaches in TBI is currently hampered by the limited availability of legacy and/or primary data, by incompatible data formats and standards. This chapter introduces Big Data and Big Data approaches such as text mining, artificial intelligence, and machine learning; outlines the benefits of using BD approaches; and suggests potential solutions that can help using the full potential of BD in TBI. It also identifies necessary changes of how researchers can help ushering in a new era of preclinical and clinical TBI research by recording and storing ALL the data generated and making ALL the data available for BD approaches-text mining, artificial intelligence, and machine learning so new correlations, relationships, and trends can be identified. In turn, these new information will help to develop novel diagnostics, evidence-based treatments, and improve outcomes.