Objective:To analyze and compare the hotspots and differences of domestic and foreign perinatal depression research in the past five years, and provide a reference and for the research of perinatal depression in China.Methods:The electronic databases Wanfang and Pubmed were searched, and domestic and foreign literatures on perinatal depression published from 2015 to 2019 were included. The Bicomb 2.0 bibliographic co-occurrence analysis system and gCLUTO software were used for word frequency analysis and two-way cluster analysis.Results:A total of 2 616 English articles and 1 074 Chinese articles were included. Totally 35 English and 20 Chinese high-frequency keywords were extracted for co-word cluster analysis. The results showed that English literature clustering included 5 topics: effects of perinatal depression on maternal and child health, influencing factors of perinatal depression, etiology of perinatal depression, epidemiology, diagnosis and interventions of perinatal depression, and early screening of perinatal depression; Chinese literature clustering included: effects of social support on negative emotions and quality of life in perinatal pregnant women, influencing factors of perinatal depression, effects of psychological intervention on perinatal pregnant women's negative emotions and pregnancy outcome, and effects of clinical nursing and health education on psychological state in pregnant women.Conclusions:The direction of exploration in the study of perinatal depression in China may be strengthening the discussion of perinatal depression, increasing long-term follow-up research on the offspring of women with perinatal depression, developing and improving multi-factor, multi-dimensional risk factor assessment tools for perinatal depression, and building a comprehensive management model.

OBJECTIVES: To explore the application of random forest algorithm in screening the risk factors and predictive values for postpartum depression. METHODS: We recruited the participants from a tertiary hospital between June 2017 and June 2018 in Changsha City, and followed up from pregnancy up to 4-6 weeks postpartum.Demographic economics, psychosocial, biological, obstetric, and other factors were assessed at first trimesters with self-designed obstetric information questionnaire and the Chinese version of Edinburgh Postnatal Depression Scale (EPDS). During 4-6 weeks after delivery, the Chinese version of EPDS was used to score depression and self-designed questionnaire to collect data of delivery and postpartum. The data of subjects were randomly divided into the training data set and the verification data set according to the ratio of 3꞉1. The training data set was used to establish the random forest model of postpartum depression, and the verification data set was used to verify the predictive effects via the accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC index. RESULTS: A total of 406 participants were in final analysis. Among them, 150 of whom had EPDS score ≥9, and the incidence of postpartum depression was 36.9%. The predictive effects of random forest model in the verification data set were at accuracy of 80.10%, sensitivity of 61.40%, specificity of 89.10%, positive predictive value of 73.00%, negative predictive value of 82.80%, and AUC index of 0.833. The top 10 predictive influential factors that screening by the variable importance measure in random forest model was antenatal depression, economic worries after delivery, work worries after delivery, free triiodothyronine in first trimesters, high-density lipoprotein in third trimester, venting temper to infants, total serum cholesterol and serum triglyceride in first trimester, hematocrit and serum triglyceride in third trimester. CONCLUSIONS Random forest has a great advantage in risk prediction for postpartum depression. Through comprehensive evaluation mechanism, it can identify the important influential factors for postpartum depression from complex multi-factors and conduct quantitative analysis, which is of great significance to identify the key factors for postpartum depression and carry out timely and effective intervention.
Depression, Postpartum/epidemiology* ; Female ; Humans ; Postpartum Period ; Pregnancy ; Pregnancy Trimester, Third ; Psychiatric Status Rating Scales ; Risk Factors ; Sensitivity and Specificity