Data-driven approaches: Creating applications in clinical settings
Another approach in the search for predictive biomarkers, is the use of data-driven approaches such as machine learning. These methods have been used successfully on neuroimaging and clinical data to answer questions about prognosis and treatment outcomes. In one study, using existing clinical trial data, clinical variables were used to predict remission in depression following treatment with selective serotonin re-uptake inhibitors (SSRIs)[4]. Supervised dimensionality reduction enabled large numbers of clinical variables to be reduced to a select few predictive variables, which were then fed into a machine learning algorithm to predict remission in patients with depression. Applying this algorithm had a number needed to treat (NNT) of 14 to achieve remission in depression[1]. Ultimately, machine learning methods could provide the basis for the creation of tools to support clinical decision making.