Leveraging Big Data for Personalized Treatment of Anxiety and Depression: Review and Possible Future Directions

Abstract

The combination of big data, machine learning and medical devices can be very effective in determining a personalized treatment plan, as exemplified through our literature review. We focus in particular on considering this combination within context of treatment of anxiety and depression. However, since the required amount and type of data (for determining such an individualized treatment plan) may not be available, we establish the need of developing enhanced biosensors to produce such data. We also argue for development of next generation of medical devices that can deliver such a personalized treatment plan for anxiety and depression. Moreover, evidence is presented to support the utility of bio-markers as potentially useful clinical tools to enhance treatment response for depression.