eprintid: 10183819
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/18/38/19
datestamp: 2023-12-14 10:57:21
lastmod: 2023-12-14 10:57:21
status_changed: 2023-12-14 10:57:21
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Feng, Wei
creators_name: Wu, Honghan
creators_name: Ma, Hui
creators_name: Tao, Zhenhuan
creators_name: Xu, Mengdie
creators_name: Zhang, Xin
creators_name: Lu, Shan
creators_name: Wan, Cheng
creators_name: Liu, Yun
title: Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study
ispublished: inpress
divisions: UCL
divisions: B02
divisions: DD4
keywords: EHR pre-trained model, type 2 diabetes mellitus, depression and anxiety, regional EHRs, deep learning
note: Copyright © The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
abstract: Objective:
Due to heterogeneity and limited medical data in primary healthcare services (PHS), assessing the psychological risk of type 2 diabetes mellitus (T2DM) patients in PHS is difficult. Using unsupervised contrastive pre-training, we proposed a deep learning framework named depression and anxiety prediction (DAP) to predict depression and anxiety in T2DM patients.
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Materials and Methods:
The DAP model consists of two sub-models. Firstly, the pre-trained model of DAP used unlabeled discharge records of 85 085 T2DM patients from the First Affiliated Hospital of Nanjing Medical University for unsupervised contrastive learning on heterogeneous electronic health records (EHRs). Secondly, the fine-tuned model of DAP used case–control cohorts (17 491 patients) selected from 149 596 T2DM patients’ EHRs in the Nanjing Health Information Platform (NHIP). The DAP model was validated in 1028 patients from PHS in NHIP. Evaluation included receiver operating characteristic area under the curve (ROC-AUC) and precision-recall area under the curve (PR-AUC), and decision curve analysis (DCA).
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Results:
The pre-training step allowed the DAP model to converge at a faster rate. The fine-tuned DAP model significantly outperformed the baseline models (logistic regression, extreme gradient boosting, and random forest) with ROC-AUC of 0.91±0.028 and PR-AUC of 0.80±0.067 in 10-fold internal validation, and with ROC-AUC of 0.75 ± 0.045 and PR-AUC of 0.47 ± 0.081 in external validation. The DCA indicate the clinical potential of the DAP model.
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Conclusion:
The DAP model effectively predicted post-discharge depression and anxiety in T2DM patients from PHS, reducing data fragmentation and limitations. This study highlights the DAP model’s potential for early detection and intervention in depression and anxiety, improving outcomes for diabetes patients.
date: 2023-12-07
date_type: published
publisher: Oxford University Press (OUP)
official_url: http://dx.doi.org/10.1093/jamia/ocad228
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2132777
doi: 10.1093/jamia/ocad228
medium: Print-Electronic
pii: 7462201
lyricists_name: Wu, Honghan
lyricists_id: HWWUX46
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
funding_acknowledgements: [Nanjing Life and Health Technology]; [Nanjing City Health Science and Technology]; [Jiangsu Provincial Health Commission]; [UK's Medical Research Council]; NIHR202639 [National Institute for Health Research]; [British Council]
full_text_status: public
publication: Journal of the American Medical Informatics Association
article_number: ocad228
event_location: England
issn: 1067-5027
citation:        Feng, Wei;    Wu, Honghan;    Ma, Hui;    Tao, Zhenhuan;    Xu, Mengdie;    Zhang, Xin;    Lu, Shan;         ... Liu, Yun; + view all <#>        Feng, Wei;  Wu, Honghan;  Ma, Hui;  Tao, Zhenhuan;  Xu, Mengdie;  Zhang, Xin;  Lu, Shan;  Wan, Cheng;  Liu, Yun;   - view fewer <#>    (2023)    Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study.                   Journal of the American Medical Informatics Association      , Article ocad228.  10.1093/jamia/ocad228 <https://doi.org/10.1093/jamia%2Focad228>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10183819/1/ocad228.pdf