发表机构
College of Engineering and Computer Science, VinUni-Illinois Smart Health Center, VinUniversity; School of Biomedical Engineering, International University, Vietnam National University HCMC（工程与计算机科学学院，VinUni - 伊利诺伊智能健康中心，Vin大学; 生物医学工程学院，胡志明市越南国立大学国际大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究基于Neurai-VN数据集构建可重复基准，定义四个临床相关二元分类任务，用标准化受试者交叉验证评估，在预定义特征配置下评估多种基线模型，给出各任务F1分数，为心理健康分类任务的多模态DP研究提供可重复基线。
AI 中文摘要
使用智能手机和可穿戴设备进行数字表型分析（DP）在心理健康监测方面显示出巨大潜力。然而，由于数据集的异质性和预处理管道的不一致，进展仍难以评估。在本研究中，我们基于Neurai-VN数据集构建了一个可重复的基准，该数据集是一个高分辨率、多模态数据集，包含可穿戴和智能手机设备的被动传感和主动评估，由100名越南成年人在两周内收集。该基准定义了四个临床相关的二元分类任务，使用标准化的受试者交叉验证进行评估。在预定义的特征配置下评估了代表性的线性、基于树的和神经基线模型。在五个交叉验证折叠中，健康对照与抑郁以及健康对照与临床的平均受试者水平F1分数分别达到0.71，而健康对照与焦虑以及抑郁与焦虑分别达到0.69和0.5憨。这些基准结果为未来心理健康分类任务的多模态DP研究提供了可重复的基线。
英文摘要
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.
