Can AI and ML Predict Depression?

If there’s one question I’ve been obsessed with for the past six months, it’s this:

How might AI change the way scientific progress happens? In particular, how might it help us make progress in areas of science where progress has historically been slow, like psychology or other fields of social science?

I’m not the only one thinking about this. Demis Hassabis, the founder of DeepMind who is currently leading AI at Google, is famous for saying, “Just as mathematics turned out to be the right description language for physics, we think AI will prove to be the right method for understanding biology.”

I love the idea of AI as a new language for describing and solving problems in the world that traditional scientific methods have had a hard time cracking, which I’ve been writing about a lot lately. AI allows us to predict phenomena in the world before we have scientific explanations for them. For example, there is no unifying scientific theory for depression. But AI and machine learning techniques might be able to predict when someone is going to experience depression, which could help with prevention and treatment. This is a significant advance because we can make progress on the disease without needing to uncover a universal underlying theory for what it is. 

I’ve been looking for researchers who are going down this path—and I found Eiko Fried. Dr. Fried is an associate professor in clinical psychology at Leiden University in the Netherlands who works on how to understand, measure, model, and classify mental health problems. His current research is a five-year project called WARN-D that uses statistics and machine learning techniques to try to predict depression before it happens. Eiko and his team have followed 2,000 students living in the Netherlands for two years, using the students’ smartwatches and smartphones to gather moment-by-moment data about them. They hope that once this project is done they’ll be able to more reliably predict when depression might occur—before it does.  

Dr. Fried’s research focuses on the view that depression and other mental illnesses are complex, dynamical systems, rather than clear-cut categories with simple causes. 

We had a wide-ranging conversation about the role of explanations and predictions in science, why many areas of science—particularly psychology—have struggled to make progress, the role of machine learning and AI in scientific research, and how his research is advancing our ability to both predict—and explain mental illnesses.

If you want to listen to this interview as a podcast, it’s available here: