Depression, affecting millions in the U.S. alone, poses serious emotional and physical challenges, including symptoms like sadness, irritability, and in severe cases, suicidal thoughts. Finding the right antidepressant medication for individuals diagnosed with depression, a complex and often lengthy process, may soon become more efficient with the help of a computer algorithm developed by researchers at Mayo Clinic and the University of Illinois at Urbana-Champaign.

This technology, which integrates clinical data and genomic information to tailor treatment decisions. Named Almond, the AI framework identifies patterns in patients’ symptoms and responses to antidepressants, enabling timely adjustments to treatment plans.

The new algorithm, tested on nearly 2,000 depression patients, demonstrated a remarkable accuracy rate of over 72% in predicting treatment response.

The study focused on patients starting treatment with SSRIs, the most commonly prescribed antidepressants. By analyzing symptom profiles and treatment outcomes, the algorithm identified specific depressive symptoms and improvement thresholds crucial for predicting treatment response.

Dr. Bobo emphasizes the algorithm’s interpretability, enabling clinicians to easily incorporate its predictions into their decision-making process during brief patient visits. This collaborative effort between computer scientists and clinicians signifies a significant step towards personalized medicine for depression treatment.

The researchers envision broader applications for the algorithm, including its integration into primary care settings to expedite mental health consultations for nonresponsive cases. Additionally, ongoing efforts aim to validate the algorithm’s effectiveness in real-world clinical practice.

Overall, the AI algorithm presents a promising tool for enhancing depression treatment outcomes by individualizing therapy decisions based on patients’ unique characteristics and responses.

Article written by Susan Murphy



Mayo clinic