These researchers want to stop treating depression with trial and error | Massive - Themes - Depression

Photo: Lauren Mackenzie Reynolds

"A promising startup is using deep learning to tailor treatments to patients" according to Lauren Mackenzie Reynolds, Neuroscience, McGill University.

Photo: Timothy Meinberg on Unsplash

Depression is the leading cause of disability in the world, and the World Health Organization estimates that over 4 percent of the world’s population suffers from depression – a whopping 322 million people.

Yet the largest clinical trial on depression to date, the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial, found that only one third of patients get better after their first course of treatment, and that patients are less and less likely to improve as more treatments, including things like SSRIs and cognitive behavioral therapy, are tried.

In part, this bleak pattern of treatment response may be because depression is an incredibly heterogeneous disorder. To meet the criteria for depression set out in the Diagnostic and Statistical Manual (DSM) a patient could be sleeping too much or too little; they could have no appetite or gain a significant amount of weight. While it’s easy to imagine that these patients may require different courses of treatment, there are not currently effective tools to predict treatment response. Treating depression is typically a process of trial and error, and unlike other medical disciplines, advancements in precision psychiatry have been slow to develop.

Deep learning and depression 
“It takes time – that’s two weeks, three weeks, four weeks, and they come back and they aren’t doing better yet and you need to try something else,” explains David Benrimoh, a psychiatry resident at McGill University and the chief executive and medical officer of aifred health, a Montreal-based startup that hopes to change this.

The aifred health team, originally comprised mostly of undergraduate students at McGill university but already vying with established companies, hopes that by applying the power of deep learning to assess biomarkers for depression, they can determine which treatment will be most effective for each individual patient, eliminating the often long period of trial and error between diagnosis and relief.
 
“I had a patient once – he had been treated for over a decade for depression,” Benrimoh continued. 

“It took years to find a treatment that worked … and he had been through almost every conceivable treatment, before he got there, some of which have significant side effects. So it was years of suffering and finally we found something.. … Clearly there is something that would have worked for him, so wouldn’t it have been nice if we had some way to know what that would have been earlier on?”

Aifred is designed to complement existing diagnostic tools and help physicians choose the treatment that, based on the patient’s profile and how that treatment has performed in thousands of other patients, will provide the best outcome. Sonia Israel, a co-founder and director of scientific partnerships (and a colleague of mine at McGill), tells me that data points could include everything from genetic, metabolic, or pharmacokinetic profiles, to neuroimaging, to any sort of peripheral marker from blood or urine.

“The power of deep learning is that it doesn’t need every feature to be filled,” she says. “It works relatively well with missing data.” But the more data available, the more precise the result...

Using deep learning with psychiatric data sets is still a new field.
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Source: Massive