Association between depressive symptoms and diagnosis of diabetes and its complications: A network analysis in electronic health records

Cheng Wan; Wei Feng; Renyi Ma; Hui Ma; Junjie Wang; Ruochen Huang; Xin Zhang; Mang Jing; Hao Yang; Haoran Yu; Yun Liu

doi:10.3389/fpsyt.2022.966758

Association between depressive symptoms and diagnosis of diabetes and its complications: A network analysis in electronic health records

Front Psychiatry. 2022 Sep 23:13:966758. doi: 10.3389/fpsyt.2022.966758. eCollection 2022.

Authors

Cheng Wan¹, Wei Feng¹, Renyi Ma¹, Hui Ma², Junjie Wang¹, Ruochen Huang¹, Xin Zhang^{1

3}, Mang Jing³, Hao Yang², Haoran Yu², Yun Liu^{1

3}

Affiliations

¹ Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
² Department of Medical Psychology, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
³ Department of Information, The First Affiliated Hospital, Nanjing Medical University, Nanjing, China.

Abstract

Objectives: Diabetes and its complications are commonly associated with depressive symptoms, and few studies have investigated the diagnosis effect of depressive symptoms in patients with diabetes. The present study used a network-based approach to explore the association between depressive symptoms, which are annotated from electronic health record (EHR) notes by a deep learning model, and the diagnosis of type 2 diabetes mellitus (T2DM) and its complications.

Methods: In this study, we used anonymous admission notes of 52,139 inpatients diagnosed with T2DM at the first affiliated hospital of Nanjing Medical University from 2008 to 2016 as input for a symptom annotation model named T5-depression based on transformer architecture which helps to annotate depressive symptoms from present illness. We measured the performance of the model by using the F1 score and the area under the receiver operating characteristic curve (AUROC). We constructed networks of depressive symptoms to examine the connectivity of these networks in patients diagnosed with T2DM, including those with certain complications.

Results: The T5-depression model achieved the best performance with an F1-score of 91.71 and an AUROC of 96.25 compared with the benchmark models. The connectivity of depressive symptoms in patients diagnosed with T2DM (p = 0.025) and hypertension (p = 0.013) showed a statistically significant increase 2 years after the diagnosis, which is consistent with the number of patients diagnosed with depression.

Conclusion: The T5-depression model proposed in this study can effectively annotate depressive symptoms in EHR notes. The connectivity of annotated depressive symptoms is associated with the diagnosis of T2DM and hypertension. The changes in the network of depressive symptoms generated by the T5-depression model could be used as an indicator for screening depression.

Keywords: depressive symptoms; diabetes complication; natural language processing; network analysis; type 2 diabetes mellitus.