Prediction of efficacy and tolerability of antidepressants and mood stabilizers

Prediction of efficacy and tolerability of antidepressants and mood stabilizers

Antidepressants and mood stabilizers are drugs for the treatment of psychiatric disorders such as depression and bipolar disorder. Psychiatrists aim to recommend an optimal drug depending on the individual symptoms and risk of adverse events. The drug would ideally improve the symptoms with a low risk of adverse events.Individualized prediction of the response to a drug and the occurrence of adverse events could improve psychiatric treatment but the identification of predictors is not well-established and remains a challenge. Machine learning techniques are statistical models that could help to identify these predictors. We aim to identify predictors, which are associated with a better treatment response and lower risk of adverse events. To this end, we will assess predictors in a comprehensive dataset of drug trials of antidepressants and mood stabilizers in affective disorders with machine learning techniques.

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