A machine learning approach can help clinicians to better predict patients’ response to antidepressants, according to new research highlighted on Tuesday.
Current clinical practice requires trial and error to determine which drug works best for each individual. But this can force patients to suffer through severe depression for months, leading to loss of employment, marriage or life.
Researchers led by UT Southwestern Medical Center championed a novel method, with a group of subjects receiving the common antidepressant sertraline while a control group took a placebo. Those in the study group then tried a different drug, bupropion, if they had not responded after eight weeks. A research team gathered magnetic resonance images from 300 participants brains’ while performing a “reward task” within the scanner to assess changes in function.
UTSW experts used this information to create artificial intelligence models highlighting changes in the brain that predict responses to the medications. While neuroscientists might typically explain 15% of variance in symptom relief, UTSW scientists were able to explain 48% of the variance for sertraline and 34% for bupropion. Even 20% is “huge,” according to a Nov. 9 announcement from the Dallas-based institution.
Source: https://aiin.healthcare/topics/diagnostics/machine-learning-predict-response-antidepressants