用潜在空间融合比早期融合更准，能更好预测抑郁症状。
Latent Space Data Fusion Outperforms Early Fusion in Multimodal Mental Health Digital Phenotyping Data
- 在潜在空间整合多模态数据，捕捉复杂非线性关系。
- 模型误差降低10.7%，决定系数提升8.4%，表现更稳定。
- 适合临床研究者和数字健康算法开发者参考。
抑郁症和焦虑症等精神疾病亟需更优的早期检测与个性化干预方法。传统预测模型多依赖单模态数据或早期融合策略，难以捕捉精神健康数据的复杂多模态特性。本研究基于BRIGHTEN临床试验数据，评估了中间（潜在空间）融合在预测每日抑郁症状（PHQ-2评分）中的表现。对比了随机森林（RF）实现的早期融合与基于自编码器和神经网络的联合模型（CM）实现的中间融合。数据包含智能手机行为、人口统计学及临床特征。在多个时间切分和数据流组合下进行实验，采用均方误差（MSE）和决定系数（R²）评估性能。结果显示，CM在所有设置中均优于RF和线性回归（LR）基线：MSE为0.4985（RF为0.5305），R²为0.4695（RF为0.4356）。RF模型表现出过拟合，训练与测试性能差距显著；而CM保持良好泛化能力。当所有模态数据在CM中融合时表现最佳，凸显潜在空间融合在复杂精神数据中捕捉非线性交互的优势。结论表明，潜在空间融合是多模态精神健康数据预测的稳健替代方案。未来工作应探索模型可解释性与个体水平预测，以支持临床部署。
原文摘要 · Abstract (English)
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.
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