标题： 潜在空间数据融合在多模态心理健康数字表型数据中优于早期融合 显示英文标题

标题： Latent Space Data Fusion Outperforms Early Fusion in Multimodal Mental Health Digital Phenotyping Data

摘要： 背景：抑郁症和焦虑症等精神疾病需要改进早期检测和个性化干预的方法。传统的预测模型通常依赖于单模态数据或早期融合策略，这些方法无法捕捉精神科数据的复杂多模态特性。先进的整合技术，如中间（潜在空间）融合，可能提供更好的准确性和临床实用性。方法：使用BRIGHTEN临床试验的数据，我们评估了中间（潜在空间）融合在预测日常抑郁症状（PHQ-2评分）中的表现。我们比较了使用随机森林（RF）模型实现的早期融合和通过自编码器和神经网络实现的中间融合组合模型（CM）。数据集包括行为（基于智能手机）、人口统计学和临床特征。实验在多个时间分割和数据流组合上进行。性能通过均方误差（MSE）和决定系数（R2）进行评估。结果：CM在所有设置中都优于RF和线性回归（LR）基线，实现了更低的MSE（0.4985 vs. 0.5305 with RF）和更高的R2（0.4695 vs. 0.4356）。RF模型表现出过拟合的迹象，训练和测试性能之间存在较大差距，而CM保持了一致的泛化能力。当在CM中整合所有数据模态时性能最佳（与RF相反），这强调了潜在空间融合在捕捉复杂精神科数据中非线性交互方面的价值。结论：潜在空间融合为多模态心理健康数据的预测提供了稳健的替代传统融合方法。未来的工作应探索模型可解释性和个体层面的预测，以用于临床部署。 显示英文摘要

摘要： Background: Mental illnesses such as depression and anxiety require improved methods for early detection and personalized intervention. Traditional predictive models often rely on unimodal data or early fusion strategies that fail to capture the complex, multimodal nature of psychiatric data. Advanced integration techniques, such as intermediate (latent space) fusion, may offer better accuracy and clinical utility. Methods: Using data from the BRIGHTEN clinical trial, we evaluated intermediate (latent space) fusion for predicting daily depressive symptoms (PHQ-2 scores). We compared early fusion implemented with a Random Forest (RF) model and intermediate fusion implemented via a Combined Model (CM) using autoencoders and a neural network. The dataset included behavioral (smartphone-based), demographic, and clinical features. Experiments were conducted across multiple temporal splits and data stream combinations. Performance was evaluated using mean squared error (MSE) and coefficient of determination (R2). Results: The CM outperformed both RF and Linear Regression (LR) baselines across all setups, achieving lower MSE (0.4985 vs. 0.5305 with RF) and higher R2 (0.4695 vs. 0.4356). The RF model showed signs of overfitting, with a large gap between training and test performance, while the CM maintained consistent generalization. Performance was best when integrating all data modalities in the CM (in contradistinction to RF), underscoring the value of latent space fusion for capturing non-linear interactions in complex psychiatric datasets. Conclusion: Latent space fusion offers a robust alternative to traditional fusion methods for prediction with multimodal mental health data. Future work should explore model interpretability and individual-level prediction for clinical deployment.