PMID- 36213916
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20221011
IS  - 1664-0640 (Print)
IS  - 1664-0640 (Electronic)
IS  - 1664-0640 (Linking)
VI  - 13
DP  - 2022
TI  - Association between depressive symptoms and diagnosis of diabetes and its 
      complications: A network analysis in electronic health records.
PG  - 966758
LID - 10.3389/fpsyt.2022.966758 [doi]
LID - 966758
AB  - 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.
CI  - Copyright (c) 2022 Wan, Feng, Ma, Ma, Wang, Huang, Zhang, Jing, Yang, Yu and Liu.
FAU - Wan, Cheng
AU  - Wan C
AD  - Department of Medical Informatics, School of Biomedical Engineering and 
      Informatics, Nanjing Medical University, Nanjing, China.
FAU - Feng, Wei
AU  - Feng W
AD  - Department of Medical Informatics, School of Biomedical Engineering and 
      Informatics, Nanjing Medical University, Nanjing, China.
FAU - Ma, Renyi
AU  - Ma R
AD  - Department of Medical Informatics, School of Biomedical Engineering and 
      Informatics, Nanjing Medical University, Nanjing, China.
FAU - Ma, Hui
AU  - Ma H
AD  - Department of Medical Psychology, Nanjing Brain Hospital, Nanjing Medical 
      University, Nanjing, China.
FAU - Wang, Junjie
AU  - Wang J
AD  - Department of Medical Informatics, School of Biomedical Engineering and 
      Informatics, Nanjing Medical University, Nanjing, China.
FAU - Huang, Ruochen
AU  - Huang R
AD  - Department of Medical Informatics, School of Biomedical Engineering and 
      Informatics, Nanjing Medical University, Nanjing, China.
FAU - Zhang, Xin
AU  - Zhang X
AD  - Department of Medical Informatics, School of Biomedical Engineering and 
      Informatics, Nanjing Medical University, Nanjing, China.
AD  - Department of Information, The First Affiliated Hospital, Nanjing Medical 
      University, Nanjing, China.
FAU - Jing, Mang
AU  - Jing M
AD  - Department of Information, The First Affiliated Hospital, Nanjing Medical 
      University, Nanjing, China.
FAU - Yang, Hao
AU  - Yang H
AD  - Department of Medical Psychology, Nanjing Brain Hospital, Nanjing Medical 
      University, Nanjing, China.
FAU - Yu, Haoran
AU  - Yu H
AD  - Department of Medical Psychology, Nanjing Brain Hospital, Nanjing Medical 
      University, Nanjing, China.
FAU - Liu, Yun
AU  - Liu Y
AD  - Department of Medical Informatics, School of Biomedical Engineering and 
      Informatics, Nanjing Medical University, Nanjing, China.
AD  - Department of Information, The First Affiliated Hospital, Nanjing Medical 
      University, Nanjing, China.
LA  - eng
PT  - Journal Article
DEP - 20220923
PL  - Switzerland
TA  - Front Psychiatry
JT  - Frontiers in psychiatry
JID - 101545006
PMC - PMC9543719
OTO - NOTNLM
OT  - depressive symptoms
OT  - diabetes complication
OT  - natural language processing
OT  - network analysis
OT  - type 2 diabetes mellitus
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2022/10/11 06:00
MHDA- 2022/10/11 06:01
PMCR- 2022/09/23
CRDT- 2022/10/10 04:39
PHST- 2022/06/11 00:00 [received]
PHST- 2022/08/08 00:00 [accepted]
PHST- 2022/10/10 04:39 [entrez]
PHST- 2022/10/11 06:00 [pubmed]
PHST- 2022/10/11 06:01 [medline]
PHST- 2022/09/23 00:00 [pmc-release]
AID - 10.3389/fpsyt.2022.966758 [doi]
PST - epublish
SO  - Front Psychiatry. 2022 Sep 23;13:966758. doi: 10.3389/fpsyt.2022.966758. 
      eCollection 2022.