A highly scalable deep learning language model for common risks prediction among psychiatric inpatients
Abstract Background There 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 b...
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2025-05-01
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| author | Enzhao Zhu Jiayi Wang Guoquan Zhou Chunbo Li Fazhan Chen Kang Ju Liangliang Chen Yichao Yin Yi Chen Yanping Zhang Xu Zhang Xinlin Zhou Zongyuan Wang Jianping Qiu Hui Wang Weizhong Shi Feng Wang Dong Wang Zhihao Chen Jiaojiao Hou Hui Li Zisheng Ai |
| author_facet | Enzhao Zhu Jiayi Wang Guoquan Zhou Chunbo Li Fazhan Chen Kang Ju Liangliang Chen Yichao Yin Yi Chen Yanping Zhang Xu Zhang Xinlin Zhou Zongyuan Wang Jianping Qiu Hui Wang Weizhong Shi Feng Wang Dong Wang Zhihao Chen Jiaojiao Hou Hui Li Zisheng Ai |
| author_sort | Enzhao Zhu |
| collection | DOAJ |
| description | Abstract Background There 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. Methods In 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. Results Of 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. Conclusions The 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. |
| format | Article |
| id | doaj-art-1005aeebd6f74eefbf7e44025652b70b |
| institution | OA Journals |
| issn | 1741-7015 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
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| series | BMC Medicine |
| spelling | doaj-art-1005aeebd6f74eefbf7e44025652b70b2025-08-20T02:03:36ZengBMCBMC Medicine1741-70152025-05-0123111510.1186/s12916-025-04150-7A highly scalable deep learning language model for common risks prediction among psychiatric inpatientsEnzhao Zhu0Jiayi Wang1Guoquan Zhou2Chunbo Li3Fazhan Chen4Kang Ju5Liangliang Chen6Yichao Yin7Yi Chen8Yanping Zhang9Xu Zhang10Xinlin Zhou11Zongyuan Wang12Jianping Qiu13Hui Wang14Weizhong Shi15Feng Wang16Dong Wang17Zhihao Chen18Jiaojiao Hou19Hui Li20Zisheng Ai21School of Medicine, Tongji UniversitySchool of Medicine, Tongji UniversityShanghai Putuo Mental Health CenterShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineClinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Chinese-German Institute of Mental Health, Tongji UniversityShanghai Changning Mental Health Center, Changning DistrictShanghai Changning Mental Health Center, Changning DistrictShanghai Changning Mental Health Center, Changning DistrictDivision of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan UniversityShanghai Jinshan District Mental Health Center, Jinshan DistrictSchool of Medicine, Tongji UniversityLakefield College SchoolSchool of Medicine, Tongji UniversityShanghai Putuo Mental Health CenterShanghai Putuo Mental Health CenterShanghai Hospital Development CenterClinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Chinese-German Institute of Mental Health, Tongji UniversityClinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Chinese-German Institute of Mental Health, Tongji UniversityEast China University of Science and TechnologyUniversity Clinic of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen UniversityShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineDepartment of Medical Statistics, School of Medicine, Tongji UniversityAbstract Background There 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. Methods In 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. Results Of 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. Conclusions The 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.https://doi.org/10.1186/s12916-025-04150-7TransformersDeep learningSuicide riskImpulsivityPhysical restraint |
| spellingShingle | Enzhao Zhu Jiayi Wang Guoquan Zhou Chunbo Li Fazhan Chen Kang Ju Liangliang Chen Yichao Yin Yi Chen Yanping Zhang Xu Zhang Xinlin Zhou Zongyuan Wang Jianping Qiu Hui Wang Weizhong Shi Feng Wang Dong Wang Zhihao Chen Jiaojiao Hou Hui Li Zisheng Ai A highly scalable deep learning language model for common risks prediction among psychiatric inpatients BMC Medicine Transformers Deep learning Suicide risk Impulsivity Physical restraint |
| title | A highly scalable deep learning language model for common risks prediction among psychiatric inpatients |
| title_full | A highly scalable deep learning language model for common risks prediction among psychiatric inpatients |
| title_fullStr | A highly scalable deep learning language model for common risks prediction among psychiatric inpatients |
| title_full_unstemmed | A highly scalable deep learning language model for common risks prediction among psychiatric inpatients |
| title_short | A highly scalable deep learning language model for common risks prediction among psychiatric inpatients |
| title_sort | highly scalable deep learning language model for common risks prediction among psychiatric inpatients |
| topic | Transformers Deep learning Suicide risk Impulsivity Physical restraint |
| url | https://doi.org/10.1186/s12916-025-04150-7 |
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