Suggestion of Gaussian Processes for Mechanical Head Injury Data Analysis

A. Tsaturyan, V. Bezhanyan (Armenia), and L.A. Gabrielian (Australia)


Mechanical Head Injury, Data Analysis, Machine Learning, Gaussian Processes.


Mechanical head injury is currently the most common neurological disorder in population under the age of 50 [1]. In most industrialized countries mechanical head injury often is considered as a devastating health care epidemic. In comparison to other diagnoses of more or less similar magnitude, head injury is under-researched due to several reasons such as luck of enough funding and the luck of a medical specialty dedicated to head injury. Research of head injury is more hindered by such practical or theoretical limitations as difficulties in testing patients and gaining enough data. The reason for this is that patients often present with varied neuropathology and psychology, which makes it hard to isolate specific areas of deficit or preservation and do specific or accurate enough data analysis. Considering the complexity of mechanical head injury and difficulties in data obtaining for making diagnoses and choosing better management, machine learning has a potential to be a very helpful tool for helping in both data analyzing and choosing better management strategies. We discuss the complexity of mechanical head injury including diagnoses, management and prognoses and try to elucidate the potential of application of Gaussian processes to head injury data analysis.

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