Big Data in traumatic brain injury; promise and challenges

Abstract

Traumatic brain injury (TBI) is a spectrum disease of overwhelming complexity, the research of which generates enormous amounts of structured, semi-structured and unstructured data. This resulting big data has tremendous potential to be mined for valuable information regarding the "most complex disease of the most complex organ". Big data analyses require specialized big data analytics applications, machine learning and artificial intelligence platforms to reveal associations, trends, correlations and patterns not otherwise realized by current analytical approaches. The intersection of potential data sources between experimental TBI and clinical TBI research presents inherent challenges for setting parameters for the generation of common data elements and to mine existing legacy data that would allow highly translatable big data analyses. In order to successfully utilize big data analyses in TBI, we must be willing to accept the messiness of data, collect and store all data and give up causation for correlation. In this context, coupling the big data approach to established clinical and pre-clinical data sources will transform current practices for triage, diagnosis, treatment and prognosis into highly integrated evidence-based patient care.

Keywords: artificial intelligence; big data; big data analytics; machine learning; traumatic brain injury.

Figure 1.. Overview of potential Big Data Analytics approaches in experimental traumatic brain injury.

Examples of data sources (see also [42]) and potential application using BDA approaches that can improve modeling, understanding the pathobiology and translatability between experimental and clinical TBI. AI: Artificial Intelligence; BDA: Big Data Analytics; bECF: Brain extracellular fluid; CSF: Cerebrospinal fluid; CT: Computer-assisted tomography; ML: Machine Learning; TBI: Traumatic brain injury.

Figure 2.. Overview of potential Big Data Analytics approaches in clinical traumatic brain injury.

Examples of data sources (see also [43,44]) and potential application using BDA approaches that can result in improved patient care, reduced mortality and better postinjury quality of life. AI: Artificial Intelligence; BDA: Big Data Analytics; bECF: Brain extracellular fluid; CBF: Cerebral blood flow; CSF: Cerebrospinal fluid; GCS: Glasgow Coma Scale; ICP: Intracranial pressure; ML: Machine learning; qEEG: Quantitative electroencephalography; TBI: Traumatic brain injury.