In a powerful example of how machine learning is changing healthcare, researchers from IIT Madras and the Czech Academy of Sciences have developed an AI model that can predict whether a person will respond to an antidepressant—within just one week of starting treatment.
This breakthrough could save time, reduce suffering, and improve how we treat mental health disorders worldwide.
Why This Research Matters
Choosing the right antidepressant can often take weeks or months of trial and error. Patients typically have to wait several weeks to see if a prescribed drug is effective, which can delay recovery and increase emotional stress.
But what if a machine could tell you early on if the medication will work?
The Science Behind It
Study Details:
- Participants: 176 individuals diagnosed with major depressive disorder
- Method: EEG (Electroencephalogram) readings taken at the start of treatment
- Tool: Machine Learning models trained on this EEG data
- Goal: Predict early on whether the patient will respond positively to antidepressants
Results:
- The model achieved an accuracy of 73%
- Predictions were made within the first 7 days of treatment
What’s EEG Got to Do With It?
EEG measures the brain’s electrical activity using small electrodes placed on the scalp. Changes in this activity can reflect how the brain responds to medication—long before noticeable changes in mood appear.
Using this data, the machine learning model finds patterns in brain signals that correlate with positive or negative responses to medication.
Expert Insight:
Dr. Raghavendra from IIT Madras said:
“Our model may help clinicians make faster, more informed decisions, reducing the guesswork in treating depression.”
What This Means for the Future
This technology could:
- Reduce the time to effective treatment
- Improve mental health outcomes
- Personalize antidepressant therapies
- Reduce healthcare costs through fewer failed treatment trials