As modern medicine becomes increasingly personalized, psychiatry lags behind, using poorly-understood drugs and therapies to treat mental disorders. With the advent of methods that capture large quantities of data, such as genome-wide analyses or fMRI, machine learning (ML) approaches have become prominent in neuroscience. This is promising for studying the brain’s function, but perhaps more importantly, these techniques can potentially predict the onset of disorder and treatment response. Experimental approaches that use naive machine learning algorithms have dominated research in computational psychiatry over the past decade. In a critical review and analysis, I argue that biologically realistic approaches will be more effective in clinical practice, and research trends should reflect this. Hybrid models are considered, and a brief case study on major depressive disorder is presented. Finally, I propose a novel four-step approach for the future implementation of computational methods in psychiatric clinics.
"Examining the Viability of Computational Psychiatry: Approaches into the Future,"
The Yale Undergraduate Research Journal: Vol. 2
, Article 35.
Available at: https://elischolar.library.yale.edu/yurj/vol2/iss1/35