Which Drug Helps Your Depression? AI System Matches Patients With the Right Depression Drug

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Animated charts show the results of a machine learning model that predicts the best depression drugs for a patient.

People seeking treatment for depression often experiment with different medications for months before finding one that works. Machine learning may remove some of the guesswork.

What’s new: Deep learning can predict how patients will respond to two antidepressant medicines, according to a study led by Albert Montillo and Madhukar Trivedi at University of Texas Southwestern Medical Center.

Key Insight: Patients with depression show various patterns of depressed brain activity in brain scans. At the same time, they vary in their reported responses to different drugs. Given brain scans of depressed people and their reports of effective treatment, a neural network can learn to match patients with medications likely to relieve their symptoms.

How it works: The authors trained separate vanilla neural networks to predict the change in patients’ depression levels after treatment with each of two drugs as well as placebo.

  • The authors trained and tested their models on data from two clinical trials. The first included 222 patients who had been diagnosed with major depressive disorder. About half received sertraline (Zoloft), and the other half received a placebo. The second included 37 participants in the first trial who had not responded to sertraline. They received bupropion (Wellbutrin) instead.
  • The dataset included 95 clinical and demographic features such as suicide risk, level of anxiety, race, and age.
  • It also included each patient’s self-reported depression level at the beginning and end of an eight-week treatment period.
  • Before undergoing treatment, the patients had received functional magnetic resonance imaging (fMRI) scans, which indicate neuronal activity, while playing a number-guessing game that triggers brain functions known to be altered by depression. The authors augmented the scans using a method that changes them in a realistic manner. They partitioned the real and synthetic scans into 200 regions and quantified brain activity using three metrics, yielding 600 features per scan.

Results: The authors evaluated their models on held-out data according to R2 value, a measure of performance in which 100 percent is perfect. The sertraline model achieved an R2 value of 48 percent. The bupropion model achieved 34 percent. Techniques that use brain scans to predict a patient’s response to drugs without deep learning have achieved R2 values around 15 percent, Montillo told The Batch.

Why it matters: Millions of adults suffer from major depression, and one-third of those try at least three drugs before settling on one. Moreover, many doctors are influenced by outcomes they observe in a handful of patients and aren’t able to systematically analyze data from a large cohort. Reliable predictions about which medicines are likely to work best — even if they’re far from perfectly accurate — could make a difference.

We’re thinking: Bringing this work into clinical practice would require training models to classify responses to many other antidepressants. The authors plan to apply their method to drugs beyond the two in this study, and we look forward to their progress.


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