Machine Learning in the Treatment of Depression
A brief overview of the current landscape in 2020
Machine learning is a hot topic that has permeated numerous public and private industries, as well as a diverse spectrum of academic disciplines, extending far beyond its humble computer science origins. Machine learning techniques are generally considered a subset of the broader field of artificial intelligence (AI), although these two terms are sometimes used interchangeably. Health care is one such industry that has attempted to apply machine learning techniques to a multitude of tasks. Machine learning is by far the most prevalent application of AI within the health care industry, including both physical health [1], and mental health [2, 3]. The goals of machine learning applications within healthcare generally strive to enhance clinical understanding and/or improve patient care. More specifically, there is a growing body of research focused on using machine learning to improve patient screening, diagnosis, clinical decision making, and specific treatment outcomes [1, 2, 4].
The application of machine learning to mental health lagged a bit behind its elder sibling physical health, however, we have seen a rapidly growing body of research in recent years applied to various facets of mental health treatment.
