Feng Wei, Wu Honghan, Ma Hui, Yin Yuechuchu, Tao Zhenhuan, Lu Shan, Zhang Xin, Yu Yun, Wan Cheng, Liu Yun
Department of Information, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, Jiangsu, China; Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China; Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China; Wuxi People's Hospital, Wuxi, Jiangsu, China.
Institute of Health Informatics, University College London, London, United Kingdom.
Int J Med Inform. 2025 Apr;196:105801. doi: 10.1016/j.ijmedinf.2025.105801. Epub 2025 Jan 22.
Depression and anxiety are prevalent mental health conditions among individuals with type 2 diabetes mellitus (T2DM), who exhibit unique vulnerabilities and etiologies. However, existing approaches fail to fully utilize regional heterogeneous electronic health record (EHR) data. Integrating this data can provide a more comprehensive understanding of depression and anxiety in T2DM patients, leading to more personalized treatment strategies.
This study aims to develop and validate a deep learning model, the Regional EHR for Depression and Anxiety Prediction Model (REDAPM), using regional EHR data to predict depression and anxiety in patients with T2DM.
A case-control development and validation study was conducted using regional EHR data from the Nanjing Health Information Center (NHIC). Two retrospective, matched (1:3) datasets were constructed from the full cohort for the model's internal and external validation. These two datasets were selected from the NHIC data of 2020 and 2022, respectively. The REDAPM incorporates both structured and unstructured EHR data, capturing the temporal dependency of clinical events. The performance of REDAPM was compared to a set of baseline models, evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and the area under the precision-recall curve (PR-AUC). Subgroup, ablation, and interpretation analyses were conducted to identify relevant clinical features available from EHRs.
The internal and external validation datasets comprised 24,724 and 34,340 patients, respectively. The REDAPM outperformed baseline models in both datasets, achieving ROC-AUC scores of 0.9029±0.008 and 0.7360±0.005, and PR-AUC scores of 0.8124±0.011 and 0.5504±0.009. Ablation and subgroup experiments confirmed the significant contribution of patients' medical history text to the model's performance. Integrated gradient score analysis identified the predictive importance of other mental disorders.
The REDAPM effectively leverages the heterogeneous characteristics of regional EHR data, demonstrating strong predictive performance for depression onset in diabetic patients. It also shows potential for discovering significant clinical features, indicating considerable promise for clinical utility.
抑郁症和焦虑症在2型糖尿病（T2DM）患者中是普遍存在的心理健康问题，这些患者表现出独特的脆弱性和病因。然而，现有的方法未能充分利用区域异质性电子健康记录（EHR）数据。整合这些数据可以更全面地了解T2DM患者的抑郁和焦虑情况，从而制定更个性化的治疗策略。
本研究旨在开发并验证一种深度学习模型，即用于抑郁和焦虑预测的区域电子健康记录模型（REDAPM），利用区域EHR数据预测T2DM患者的抑郁和焦虑情况。
使用来自南京健康信息中心（NHIC）的区域EHR数据进行病例对照的开发和验证研究。从整个队列中构建了两个回顾性匹配（1:3）数据集，用于模型的内部和外部验证。这两个数据集分别选自NHIC 2020年和2022年的数据。REDAPM整合了结构化和非结构化EHR数据，捕捉临床事件的时间依赖性。将REDAPM的性能与一组基线模型进行比较，使用受试者操作特征曲线下面积（ROC-AUC）和精确召回率曲线下面积（PR-AUC）进行评估。进行亚组、消融和解释分析，以确定EHR中可用的相关临床特征。
内部和外部验证数据集分别包含24724例和34340例患者。REDAPM在两个数据集中均优于基线模型，ROC-AUC得分分别为0.9029±0.008和0.7360±0.005，PR-AUC得分分别为0.8124±0.011和0.5504±0.009。消融和亚组实验证实了患者病史文本对模型性能的显著贡献。综合梯度得分分析确定了其他精神障碍的预测重要性。
REDAPM有效地利用了区域EHR数据的异质性特征，对糖尿病患者的抑郁发作具有很强的预测性能。它还显示出发现重要临床特征的潜力，表明具有相当大的临床应用前景。
