A Multimodal Depression Consultation Dataset of Speech and Text with HAMD-17 Assessments

The global surge in depression rates, notably severe in China with over 95 million affected, underscores a dire public health issue. This is exacerbated by a critical shortfall in mental health professionals, highlighting an urgent call for innovative approaches. The advancement of Artificial Intelligence (AI), particularly Large Language Models, offers a promising solution by improving mental health diagnostics. However, there is a lack of real data for reliable training and accurate evaluation of AI models. To this end, this paper presents a high-quality multimodal depression consultation dataset, namely Parallel Data of Depression Consultation and Hamilton Depression Rating Scale (PDCH). The dataset is constructed based on clinical consultations from Beijing Anding Hospital, which provides audio recording and transcribed text, as well as corresponding HAMD-17 scales annotated by professionals. The dataset contains 100 consultations and the audio exceeds 2,937 minutes. Each of them is about 30-min long with more than 150 dialogue turns. It enables to fill the gap in mental health services and benefit the creation of more accurate AI models.

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Publication Details

Journal
Scientific Data
Published
2025-09-29
DOI
https://doi.org/10.1038/s41597-025-05817-9
Citations
3
Primary Topic
Mental Health via Writing
Type
article
Field-Weighted Citation Impact
7.32

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article

A Multimodal Depression Consultation Dataset of Speech and Text with HAMD-17 Assessments

Chenming Zhang, Jinjie Xu, Wenqing Jin, Yanping Ren et al.
3 citations
Scientific Data
Mental Health via Writing
7.32
article

A Multimodal Depression Consultation Dataset of Speech and Text with HAMD-17 Assessments

Chenming Zhang, Jinjie Xu, Wenqing Jin, Yanping Ren, Wei Chen, Yan Liu, Kang Liu, Dan Wang, Xue Wang, Shuang Xu, Wei Wang, Miao Xu, Pengfei Cao, Rena Li, Yuanzhe Zhang, Jun Zhao, Wen Wang
article en
3 citations

Abstract

The global surge in depression rates, notably severe in China with over 95 million affected, underscores a dire public health issue. This is exacerbated by a critical shortfall in mental health professionals, highlighting an urgent call for innovative approaches. The advancement of Artificial Intelligence (AI), particularly Large Language Models, offers a promising solution by improving mental health diagnostics. However, there is a lack of real data for reliable training and accurate evaluation of AI models. To this end, this paper presents a high-quality multimodal depression consultation dataset, namely Parallel Data of Depression Consultation and Hamilton Depression Rating Scale (PDCH). The dataset is constructed based on clinical consultations from Beijing Anding Hospital, which provides audio recording and transcribed text, as well as corresponding HAMD-17 scales annotated by professionals. The dataset contains 100 consultations and the audio exceeds 2,937 minutes. Each of them is about 30-min long with more than 150 dialogue turns. It enables to fill the gap in mental health services and benefit the creation of more accurate AI models.

Scientific DataVol. 12(1)
Capital Medical University (CN), Shandong Institute of Automation (CN), Beijing Academy of Artificial Intelligence (CN), Institute of Automation (CN), Beijing Anding Hospital (CN), National Science Library (CN), University of Chinese Academy of Sciences (CN)
Chinese Academy of Sciences
Openalex Percentile: Top 3%
Mental Health via Writing
7.32
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