While existing depression prediction methods based on deep learning show promise, their practical application is hindered by the lack of trustworthiness, as these deep models are often deployed as \textit{black box} models, leaving us uncertain about the confidence of the model predictions. For high-risk clinical applications like depression prediction, uncertainty quantification is essential in decision-making. In this paper, we introduce conformal depression prediction (CDP), a depression prediction method with uncertainty quantification based on conformal prediction (CP), giving valid confidence intervals with theoretical coverage guarantees for the model predictions. CDP is a plug-and-play module that requires neither model retraining nor an assumption about the depression data distribution. As CDP provides only an average coverage guarantee across all inputs rather than per-input performance guarantee, we further propose CDP-ACC, an improved conformal prediction with approximate conditional coverage. CDP-ACC firstly estimates the prediction distribution through neighborhood relaxation, and then introduces a conformal score function by constructing nested sequences, so as to provide a tighter prediction interval for each specific input. We empirically demonstrate the application of CDP in uncertainty-aware depression prediction, as well as the effectiveness and superiority of CDP-ACC on the AVEC 2013 and AVEC 2014 datasets.

翻译：尽管现有的基于深度学习的抑郁预测方法展现出潜力，但其实际应用因缺乏可信度而受到阻碍，因为这些深度模型通常被部署为\textit{黑箱}模型，使得我们无法确定模型预测的置信度。对于抑郁预测这类高风险临床应用，不确定性量化在决策中至关重要。本文提出共形抑郁预测（CDP），一种基于共形预测（CP）的、具有不确定性量化的抑郁预测方法，能为模型预测提供具有理论覆盖保证的有效置信区间。CDP是一个即插即用模块，既不需要模型重新训练，也不需要对抑郁数据分布做出假设。由于CDP仅提供所有输入的平均覆盖保证，而非针对每个输入的性能保证，我们进一步提出CDP-ACC，一种具有近似条件覆盖的改进共形预测方法。CDP-ACC首先通过邻域松弛估计预测分布，然后通过构建嵌套序列引入共形评分函数，从而为每个特定输入提供更紧密的预测区间。我们在AVEC 2013和AVEC 2014数据集上，实证展示了CDP在不确定性感知抑郁预测中的应用，以及CDP-ACC的有效性和优越性。