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<p>We introduce an artificial intelligence model to personalize treatment in major depression, which was deployed in the Artificial Intelligence in Depression: Medication Enhancement Study. We predict probabilities of remission across multiple pharmacological treatments, validate model predictions, and examine them for biases. Data from 9042 adults with moderate to severe major depression from antidepressant clinical trials were used to train a deep learning model. On the held-out test-set, the model demonstrated an AUC of 0.65, outperformed a null model (<i>p</i> = 0.01). The model increased population remission rate in hypothetical and actual improvement testing. While the model identified escitalopram as generally outperforming other drugs (consistent with the input data), there was otherwise significant variation in drug rankings. The model did not amplify potentially harmful biases. We demonstrate the first model capable of predicting outcomes for 10 treatments, intended to be used at or near the start of treatment to personalize treatment selection.</p>

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Development of the treatment prediction model in the artificial intelligence in depression – medication enhancement study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月23日

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  • David Benrimoh,
  • Caitrin Armstrong,
  • Joseph Mehltretter,
  • Robert Fratila,
  • Kelly Perlman,
  • Sonia Israel,
  • Adam Kapelner,
  • Sagar V. Parikh,
  • Jordan F. Karp,
  • Katherine Heller,
  • Gustavo Turecki

摘要

We introduce an artificial intelligence model to personalize treatment in major depression, which was deployed in the Artificial Intelligence in Depression: Medication Enhancement Study. We predict probabilities of remission across multiple pharmacological treatments, validate model predictions, and examine them for biases. Data from 9042 adults with moderate to severe major depression from antidepressant clinical trials were used to train a deep learning model. On the held-out test-set, the model demonstrated an AUC of 0.65, outperformed a null model (p = 0.01). The model increased population remission rate in hypothetical and actual improvement testing. While the model identified escitalopram as generally outperforming other drugs (consistent with the input data), there was otherwise significant variation in drug rankings. The model did not amplify potentially harmful biases. We demonstrate the first model capable of predicting outcomes for 10 treatments, intended to be used at or near the start of treatment to personalize treatment selection.