Machine learning depression
Depression affects many people during their lifetime and is a substantial public health problem, causing tremendous human suffering and costs to society. Therefore, improving treatment and early detection of depression is an absolute priority. However, despite numerous investments, progress in depression research has stagnated: we still know very little about the underlying mechanisms, and in practice clinicians struggle to determine a patient’s prognosis and optimal treatment. In this project we aim to provide predictive and flexible machine learning based models that can aid the decision making process.