跨年龄人群抑郁检测模型表现稳定，老人数据仍有效
Cross-Demographic Portability of Deep NLP-Based Depression Models
- 用年轻人数据训练的抑郁识别模型，迁移到老人数据上仍保持高精度
- 在老人数据集上达到AUC=0.76，部分稳定患者群体达AUC=0.81
- 证明语音情绪分析模型具备跨年龄泛化能力，适合老龄化应用
深度学习模型在行为健康领域日益受到关注，但其在不同人群间的泛化能力尚不明确。本文研究基于自然语言处理（NLP）的抑郁检测模型在两个年龄差异显著语料库上的迁移性能。第一个大规模语料库来自年轻群体，用于训练抑郁预测模型，在同龄测试集上表现良好，AUC=0.82。将该模型应用于来自养老社区的老年人语料库时，尽管存在显著人口学差异，性能仅出现小幅下降，测试结果AUC=0.76。值得注意的是，在老年群体中，对病情稳定者子集的预测表现更优，AUC达到0.81。研究讨论了基于语音的应用在跨人口群体中的可迁移性意义。
原文摘要 · Abstract (English)
Deep learning models are rapidly gaining interest for real-world applications in behavioral health. An important gap in current literature is how well such models generalize over different populations. We study Natural Language Processing (NLP) based models to explore portability over two different corpora highly mismatched in age. The first and larger corpus contains younger speakers. It is used to train an NLP model to predict depression. When testing on unseen speakers from the same age distribution, this model performs at AUC=0.82. We then test this model on the second corpus, which comprises seniors from a retirement community. Despite the large demographic differences in the two corpora, we saw only modest degradation in performance for the senior-corpus data, achieving AUC=0.76. Interestingly, in the senior population, we find AUC=0.81 for the subset of patients whose health state is consistent over time. Implications for demographic portability of speech-based applications are discussed.
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