Detecting Depression with Machine Learning: The Why and the How

Over the past decade, there has been tremendous progress in using artificial intelligence to better understand people. From natural language understanding to recognizing people’s emotions, deep learning has enabled a whole new set of applications that will profoundly change the way we interact with machines.

One of the most promising applications is in mental health, where machine learning could provide great value to both doctors and patients. Especially a lot of attention has been given to automatic depression detection, and how to use machine learning to help with screening, diagnosis, and monitoring. In this post, I’d like to give an overview of what’s being done and the benefits of a more automated approach.

Cases of depression increased by nearly a fifth during 2005-15. It is estimated that around 15% of people worldwide experience depression at some point in their lifetime. The Global Burden of Disease Study, which compares the number of years lost due to ill-health, disability, or early death between 369 diseases worldwide, found that years lost due to depressive disorders increased more than 60% between 1990 and 2019. This sets depression as the 6th most burdensome disorder for adults aged 25-49, before strokes or diabetes.

The Covid 19 pandemic has only made the problem more urgent. Depression and anxiety issues are surging worldwide since the beginning of the pandemic, and researchers are increasingly worried about the long-term effects of Covid on mental health.

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