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New benchmark standardizes machine learning for mental health classification

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced the Neurai-VN Benchmark, a standardized framework for evaluating machine learning models in mental health classification using digital phenotyping. This benchmark is built upon the Neurai-VN dataset, which includes passive sensing and active assessment data from 100 Vietnamese adults over two weeks. The study established baseline performance metrics, achieving F1 scores of up to 0.71 for distinguishing between healthy individuals and those with depression or clinical conditions, and 0.69 for healthy individuals versus anxiety. AI

IMPACT Standardizes evaluation for AI models in mental health monitoring, potentially accelerating research and clinical application.

RANK_REASON The cluster describes a new benchmark and dataset for machine learning in mental health classification, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark standardizes machine learning for mental health classification

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The cluster describes a new benchmark and dataset for machine learning in mental health classification, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Quoc-Cuong Pham, Hoang-Thuy-Duong Vu, Thi-Thanh-Huong Ha, Huy-Hieu Pham ·

    Neurai-VN Benchmark: Standardized Machine Learning Models for Multimodal Digital Phenotyping in Mental Health Classification

    arXiv:2607.25232v2 Announce Type: replace Abstract: Digital phenotyping (DP) using smartphones and wearable devices has shown considerable potential for mental health monitoring. However, progress remains difficult to evaluate due to heterogeneous datasets, inconsistent preproces…

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