用于重度抑郁症成人患者差异化治疗效益预测的深度学习模型的开发与验证——在抑郁症药物增强人工智能（AIDME）研究中部署
Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study
AI总结：
本研究开发并验证了一个深度学习模型，用于预测重度抑郁症成人患者对10种药物治疗的缓解概率，该模型在测试中表现优于零模型并提升了缓解率，且未放大有害偏见，已投入AIDME随机对照试验中应用。
摘要：
引言：重度抑郁症（MDD）的药物治疗依赖于试错方法。我们引入了一个旨在个性化治疗并改善结果的人工智能（AI）模型，该模型已在抑郁症药物增强人工智能（AIDME）研究中部署。目标：1）开发一个模型，能够预测患有至少中度重度抑郁症的成人在多种药物治疗下的缓解概率。2）验证模型预测并检查其是否放大了有害偏见。方法：将既往抗抑郁药物临床试验的数据标准化为统一框架，纳入了9,042名患有中至重度重度抑郁症的成人。特征选择保留了25个临床和人口统计学变量。使用贝叶斯优化，在训练集上训练了一个深度学习模型，使用验证集进行优化，并在留出的测试集上测试了一次。结果：在留出测试集的评估中，模型达到了0.65的AUC。模型在测试集上优于零模型（p = 0.01）。模型展现了临床实用性，在假设和实际改进测试中实现了人群缓解率的绝对提升。尽管模型确实识别出一种药物（escitalopram）总体上优于其他药物（与输入数据一致），但在其他方面药物排名存在显著差异。在偏见测试中，模型未放大潜在的有害偏见。结论：我们展示了首个能够为MDD患者预测10种不同治疗方案结果的模型，旨在治疗开始时或接近开始时使用以个性化治疗。该模型已在AIDME随机对照试验中投入临床实践，其结果另行报告。
英文摘要：
INTRODUCTION: The pharmacological treatment of Major Depressive Disorder (MDD) relies on a trial-and-error approach. We introduce an artificial intelligence (AI) model aiming to personalize treatment and improve outcomes, which was deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study. OBJECTIVES: 1) Develop a model capable of predicting probabilities of remission across multiple pharmacological treatments for adults with at least moderate major depression. 2) Validate model predictions and examine them for amplification of harmful biases. METHODS: Data from previous clinical trials of antidepressant medications were standardized into a common framework and included 9,042 adults with moderate to severe major depression. Feature selection retained 25 clinical and demographic variables. Using Bayesian optimization, a deep learning model was trained on the training set, refined using the validation set, and tested once on the held-out test set. RESULTS: In the evaluation on the held-out test set, the model demonstrated achieved an AUC of 0.65. The model outperformed a null model on the test set (p = 0.01). The model demonstrated clinical utility, achieving an absolute improvement in population remission rate in hypothetical and actual improvement testing. While the model did identify one drug (escitalopram) as generally outperforming the other drugs (consistent with the input data), there was otherwise significant variation in drug rankings. On bias testing, the model did not amplify potentially harmful biases. CONCLUSIONS: We demonstrate the first model capable of predicting outcomes for 10 different treatment options for patients with MDD, intended to be used at or near the start of treatment to personalize treatment. The model was put into clinical practice during the AIDME randomized controlled trial whose results are reported separately.
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