ABSTRACT
Recent developments in neurological disorder diagnosis and treatment process have notably benefitted from the use of computational methods and software support, a research field commonly referred to as computational psychiatry. The computational psychiatry research is currently split into two directions, namely, data-driven and theory-driven. The contrasting benefits and limitations of data-driven and theory-driven approaches have led to hybrid approaches that have higher future potential. This warrants the need for reviewing the current state-of-the-art research in computational psychiatry and to identify prevailing key challenges. This chapter provides insights into the current computational psychiatry practices and suggests enhancements through data analysis to improve the efficacy of current clinical practices.
