Evidence map›Paper›PMID 40437564›Full record

ArticleBMC medicine2025

A highly scalable deep learning language model for common risks prediction among psychiatric inpatients.

Enzhao Zhu, Jiayi Wang, Guoquan Zhou, Chunbo Li, Fazhan Chen, Kang Ju, Liangliang Chen, Yichao Yin, Yi Chen, Yanping Zhang and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medicine, 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.

  1. Trial
  2. Artificial Intelligence-Empowered Multimodal Learning in Psychiatry: A Scoping Review.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026
    Review
  3. Article
  4. Article
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

22 authors.

Enzhao Zhu *School of Medicine, Tongji University, Shanghai, China.
Jiayi Wang *School of Medicine, Tongji University, Shanghai, China.
Guoquan ZhouShanghai Putuo Mental Health Center, Putuo District, Shanghai, China.
Chunbo LiShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200030, China.
Fazhan ChenClinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Chinese-German Institute of Mental Health, Tongji University, Shanghai, China.
Kang JuShanghai Changning Mental Health Center, Changning District, Shanghai, China.
Liangliang ChenShanghai Changning Mental Health Center, Changning District, Shanghai, China.
Yichao YinShanghai Changning Mental Health Center, Changning District, Shanghai, China.
Yi ChenDivision of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China.
Yanping ZhangShanghai Jinshan District Mental Health Center, Jinshan District, Shanghai, China.
Xu ZhangSchool of Medicine, Tongji University, Shanghai, China.
Xinlin ZhouLakefield College School, Lakefield, ON, Canada.
Zongyuan WangSchool of Medicine, Tongji University, Shanghai, China.
Jianping QiuShanghai Putuo Mental Health Center, Putuo District, Shanghai, China.
Hui WangShanghai Putuo Mental Health Center, Putuo District, Shanghai, China.
Weizhong ShiShanghai Hospital Development Center, Shanghai, China.
Feng WangClinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Chinese-German Institute of Mental Health, Tongji University, Shanghai, China.
Dong WangClinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Chinese-German Institute of Mental Health, Tongji University, Shanghai, China.
Zhihao ChenEast China University of Science and Technology, Shanghai, China.
Jiaojiao HouUniversity Clinic of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, Aachen, 52074, Germany.
Hui Li *Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200030, China. lihuindyxs@163.com.
Zisheng Ai *Department of Medical Statistics, School of Medicine, Tongji University, Shanghai, China. azs1966@126.com.

Funding

CCS-DASET SHDC2024CRI008Changning District Municipal Commission of Health CNWJXY026Shanghai Municipal Health Commission 202340018Shanghai Putuo District Health Committee ptkwws202413
6 · The paper itself

Abstract

backgroundThere is a lack of studies exploring the performance of Transformers-based language models in common risks assessment among psychiatric inpatients. We aim to develop a scalable risk assessment model using multidimensional textualized data and test the stability, robustness, and benefit of this approach.

methodsIn this real-world cohort study, a deep learning language model was developed and validated using first hospitalized cases diagnosed with schizophrenia, bipolar disorder, and depressive disorder between January 2016 and March 2023 in three hospitals. The algorithm was externally validated on an independent testing cohort comprising 1180 patients. A total of 140 features, including first medical records (FMR), laboratory examinations, medical orders, and psychological scales, were assessed for analysis. The outcomes were short- and long-term impulsivity (STI and LTI), risk of suicide (STSS and LTSS), and need of physical restraint (STPR and LTPR) assessed by qualified nurses or clinicians. Analysis was carried out between August 2024 and June 2024. Models with different architectures and input settings were compared with each other. The area under the receiver operating characteristic curve (AUROC) was used to assess the primary performance of models. The clinical utility was determined by the net benefit under Youden's threshold.

resultsOf 7451 patients included in this study, 2982 (47.6%) were male, and the median (interquartile range) age was 42 (28-57) years. The overall incidence of outcomes was 635 (8.5%), 728 (10.5%), 659 (8.8%), 803 (10.8%), 588 (7.9%), and 728 (9.8%) for STPR, LTPR, STSS, LTSS, STI, and LTI, respectively. The multitask semi-structured Transformers-based language (SSTL) model showed more promising AUROCs (STPR: 0.915; LTPR: 0.844; STSS: 0.867; LTSS: 0.879; STI: 0.899; LTI: 0.894) in the prediction of these outcomes than single-tasked or multimodal language models and traditional structured data models. Combining FMR with other data from electronic health records led to significant improvements in the performance and clinical utility of SSTL models based on demographic, diagnosis, laboratory tests, treatment, and psychological scales.

conclusionsThe SSTL model shows potential advantages in prognostic evaluation. FMR is a strong predictor for common risks prediction and may benefit other tasks in psychiatry with minimum requirements for data and data processing.

Indexed as

Deep LearningInpatientsMental DisordersAdultBipolar DisorderCohort StudiesFemaleHumansMaleMiddle AgedRisk AssessmentSchizophreniaDeep learningImpulsivityPhysical restraintSuicide riskTransformers

Identifiers

PMID40437564
PMCPMC12121029

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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.