构建标准化基准，用多模态数据提升心理健康分类准确性。
Neurai-VN Benchmark: Standardized Machine Learning Models for Multimodal Digital Phenotyping in Mental Health Classification
- 基于越南100人两周数据，定义四类标准分类任务
- 各任务F1最高达0.71，提供可复现基线结果
- 适合心理健康与数字表型研究者参考
利用智能手机和可穿戴设备进行数字表型（DP）已成为评估心理健康（尤其是抑郁和焦虑）的有前景方法。然而，由于数据集异质性和预处理流程不一致，研究进展难以评估。本文引入基于Neurai-VN数据集的可复现机器学习基准，该数据集来自100名越南成年人为期两周的多模态数字表型数据。我们定义了四个二分类任务，并采用基于受试者交叉验证的标准评估方式。系统评估了线性、树模型和神经网络等代表性基线模型，在预定义特征组配置下的表现。五折交叉验证中，健康对照组 vs. 抑郁症和健康对照组 vs. 临床诊断的平均受试者级F1分数分别达到0.71；健康对照组 vs. 焦虑症和抑郁症 vs. 焦虑症分别为0.69和0.56。这些基线结果为未来多模态数字表型在心理健康分类中的研究提供了可复现的基准。代码已开源：https://github.com/neurai-vn/Neurai-VN-benchmark。
原文摘要 · Abstract (English)
Digital phenotyping (DP) using smartphones and wearable devices has emerged as a promising approach for assessing mental health, particularly depression and anxiety. However, progress remains difficult to evaluate because of heterogeneity across datasets and inconsistencies in preprocessing pipelines. In this work, we introduce a reproducible machine learning benchmark using the Neurai-VN dataset, a multimodal digital phenotyping dataset collected from 100 Vietnamese adults over two weeks. We define four binary classification tasks evaluated using standardized subject-wise cross-validation. Representative linear, tree-based, and neural baseline models are evaluated systematically across predefined feature-group configurations. Mean subject-level F1 scores across five cross-validation folds reached 0.71 for Healthy Control vs. Depression and Healthy Control vs. Clinical, while Healthy Control vs. Anxiety and Depression vs. Anxiety achieved 0.69 and 0.56, respectively. These baseline results provide reproducible baselines for future research on multimodal DP for mental health classification tasks. The code to reproduce the benchmark is available at https://github.com/neurai-vn/Neurai-VN-benchmark.
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