Evidence map›Paper›PMID 41022829›Full record

ArticleScientific data2025

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

Pengfei Cao, Yuanzhe Zhang, Chenxiang Zhang, Wei Chen, Yan Liu, Shuang Xu, Miao Xu, Wenqing Jin, Jinjie Xu, Dan Wang and 7 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Pengfei CaoThe Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Yuanzhe ZhangNational Science Library, Chinese Academy of Sciences, Beijing, China.
Chenxiang ZhangThe Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Wei ChenThe Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Yan LiuThe Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Shuang XuThe Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Miao XuBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Wenqing JinBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Jinjie XuBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Dan WangBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Wei WangBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Xue WangBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Wen WangBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Yanping RenBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Jun ZhaoThe Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. jzhao@nlpr.ia.ac.cn.
Rena LiBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China. renali@ccmu.edu.cn.
Kang LiuThe Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. kliu@nlpr.ia.ac.cn.

Funding

CAS | Institute of Automation, Chinese Academy of Sciences (CASIA) ZDBS-SSW-JSC006
6 · The paper itself

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.

Indexed as

DepressionArtificial IntelligenceChinaHumansReferral and ConsultationSpeech

Identifiers

PMID41022829
PMCPMC12480040

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.