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SEOE: an option graph based semantically embedding method for prenatal depression detection

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Abstract

Prenatal depression, which can affect pregnant women’s physical and psychological health and cause postpartum depression, is increasing dramatically. Therefore, it is essential to detect prenatal depression early and conduct an attribution analysis. Many studies have used questionnaires to screen for prenatal depression, but the existing methods lack attributability. To diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires, we present the semantically enhanced option embedding (SEOE) model to represent questionnaire options. It can quantitatively determine the relationship and patterns between options and depression. SEOE first quantifies options and resorts them, gathering options with little difference, since Word2Vec is highly dependent on context. The resort task is transformed into an optimization problem involving the traveling salesman problem. Moreover, all questionnaire samples are used to train the options’ vector using Word2Vec. Finally, an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from depression. To verify the model, we compare it with other deep learning and traditional machine learning methods. The experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of 0.8. The most relevant factors of depression found by SEOE are also verified in the literature. In addition, our model is of low computational complexity and strong generalization, which can be widely applied to other questionnaire analyses of psychiatric disorders.

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References

  1. WHO. World mental health report: transforming mental health for all. Geneva: WHO, 2022

    Google Scholar 

  2. Mukherjee S, Trepka M J, Pierre-Victor D, Bahelah R, Avent T. Racial/ethnic disparities in antenatal depression in the united states: a systematic review. Maternal and Child Health Journal, 2016, 20(9): 1780–1797

    Article  Google Scholar 

  3. Okagbue H I, Adamu P I, Bishop S A, Oguntunde P E, Opanuga A A, Akhmetshin E M. Systematic review of prevalence of antepartum depression during the trimesters of pregnancy. Open Access Macedonian Journal of Medical Sciences, 2019, 7(9): 1555–1560

    Article  Google Scholar 

  4. Lee A M, Lam S K, Lau S M S M, Chong C S Y, Chui H W, Fong D Y T. Prevalence, course, and risk factors for antenatal anxiety and depression. Obstetrics & Gynecology, 2007, 110(5): 1102–1112

    Article  Google Scholar 

  5. Hay D F, Pawlby S, Waters C S, Sharp D. Antepartum and postpartum exposure to maternal depression: different effects on different adolescent outcomes. Journal of Child Psychology and Psychiatry, 2008, 49(10): 1079–1088

    Article  Google Scholar 

  6. Deave T, Heron J, Evans J, Emond A. The impact of maternal depression in pregnancy on early child development. Obstetrical & Gynecological Survey, 2008, 63(10): 626–628

    Article  Google Scholar 

  7. Andersson L, Sundström-Poromaa I, Wulff M, Aström M, Bixo M. Depression and anxiety during pregnancy and six months postpartum: a follow-up study. Acta Obstetricia et Gynecologica Scandinavica, 2006, 85(8): 937–944

    Article  Google Scholar 

  8. Field T. Prenatal depression effects on early development: a review. Infant Behavior and Development, 2011, 34(1): 1–14

    Article  MathSciNet  Google Scholar 

  9. Milgrom J, Gemmill A W, Bilszta J L, Hayes B, Barnett B, Brooks J, Ericksen J, Ellwood D, Buist A. Antenatal risk factors for postnatal depression: a large prospective study. Journal of Affective Disorders, 2008, 108(1–2): 147–157

    Article  Google Scholar 

  10. Leigh B, Milgrom J. Risk factors for antenatal depression, postnatal depression and parenting stress. BMC psychiatry, 2008, 8: 1–11

    Article  Google Scholar 

  11. Cox J L, Holden J M, Sagovsky R. Detection of postnatal depression: development of the 10-item Edinburgh postnatal depression scale. British Journal of Psychiatry, 1987, 150(6): 782–786

    Article  Google Scholar 

  12. Ware J E Jr, Sherbourne C D. The MOS 36-item short-form health survey (sf-36). I. Conceptual framework and item selection. Medical Care, 1992, 30(6): 473–483

    Article  Google Scholar 

  13. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. Journal of Health and Social Behavior, 1983, 24(4): 385–396

    Article  Google Scholar 

  14. Zimet G D, Dahlem N W, Zimet S G, Farley G K. The multidimensional scale of perceived social support. Journal of Personality Assessment, 1988, 52(1): 30–41

    Article  Google Scholar 

  15. Buysse D J, Reynolds III C F, Monk T H, Berman S R, Kupfer D J. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Research, 1989, 28(2): 193–213

    Article  Google Scholar 

  16. Sidebottom A C, Harrison P A, Godecker A, Kim H. Validation of the patient health questionnaire (PHQ)-9 for prenatal depression screening. Archives of Women’s Mental Health, 2012, 15(5): 367–374

    Article  Google Scholar 

  17. Lu J, Lu Z. Development, distribution and evaluation of online tourism services in China. Electronic Commerce Research, 2004, 4(3): 221–239

    Article  Google Scholar 

  18. Lu J, Schmidt M, Lee M, Huang R. Usability research in educational technology: a state-of-the-art systematic review. Educational Technology Research and Development, 2022, 70(6): 1951–1992

    Article  Google Scholar 

  19. Liu J H. Statistical analysis methods in survey questionnaires. Shanghai Statistics, 2002, (2): 35–37

  20. Liu X X, Dong R Z. Report on satisfaction questionnaire for higher education students - mainly using SEM analysis method. Journal of Vocational Education, 2011, 27(34): 19–21

    Google Scholar 

  21. Xu R F. Selection for independent variables and regression method in logistic regression: an example analysis. Chinese Journal of Evidence-Based Medicine, 2016, 16(11): 1360–1364

    Google Scholar 

  22. Li L, Wu X, Kong M, Liu J, Zhang J. Quantitatively interpreting residents happiness prediction by considering factor–factor interactions. IEEE Transactions on Computational Social Systems, 2024, 11(1): 1402–1414

    Article  Google Scholar 

  23. Yu Y, Duan J, Li M. Fusion model for tentative diagnosis inference based on clinical narratives. Tsinghua Science and Technology, 2023, 28(4): 686–695

    Article  Google Scholar 

  24. Zhang H, Liu S, Chang H, Zhan M, Qin Q M, Zhang B, Li Z, Liu Y. Mining Magnaporthe oryzae sRNAs with potential transboundary regulation of rice genes associated with growth and defense through expression profile analysis of the pathogen-infected rice. Frontiers in Genetics, 2019, 10: 296

    Article  Google Scholar 

  25. Han X, Cao M, He J, Xu D, Liang Y, Lang X, Guan R. A comprehensive psychological tendency prediction model for pregnant women based on questionnaires. Scientific Reports, 2023, 13(1): 2

    Article  Google Scholar 

  26. Meng Z, Chen C, Zhang X, Zhao W, Cui X. Exploring fragment adding strategies to enhance molecule pretraining in AI-driven drug discovery. Big Data Mining and Analytics, 2024, doi: https://doi.org/10.26599/BDMA.2024.9020003

  27. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. In: Proceedings of the 1st International Conference on Learning Representations. 2013

  28. Yang Y, Chen H, Heidari A A, Gandomi A H. Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 2021, 177: 114864

    Article  Google Scholar 

  29. Guan R, Zhang H, Liang Y, Giunchiglia F, Huang L, Feng X. Deep feature-based text clustering and its explanation. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(8): 3669–3680

    Article  Google Scholar 

  30. Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014, arXiv preprint arXiv: 1412.3555

  31. Smith L N. Cyclical learning rates for training neural networks. In: Proceedings of 2017 IEEE winter conference on applications of computer vision (WACV). 2017, 464–472

  32. Lang X D, Wang N, Zang X D, Li J F. Survey on the pre-pregnancy psychological status of pregnant women of childbearing age and their demand for eugenic psychological counseling and guidance. China Practical Medical, 2017, 12(24): 183–185

    Google Scholar 

  33. Zung W W K. A self-rating depression scale. Archives of General Psychiatry, 1965, 12(1): 63–70

    Article  Google Scholar 

  34. Zung W W. Self-rating anxiety scale. BMC Psychiatry, 1971

  35. Whitley D. A genetic algorithm tutorial. Statistics and Computing, 1994, 4(2): 65–85

    Article  Google Scholar 

  36. Chen H, Heidari A A, Chen H, Wang M, Pan Z, Gandomi A H. Multipopulation differential evolution-assisted Harris hawks optimization: Framework and case studies. Future Generation Computer Systems, 2020, 111: 175–198

    Article  Google Scholar 

  37. Ting C K, Su C H, Lee C N. Multi-parent extension of partially mapped crossover for combinatorial optimization problems. Expert Systems with Applications, 2010, 37(3): 1879–1886

    Article  Google Scholar 

  38. Rong X. word2vec parameter learning explained. 2014, arXiv preprint arXiv: 1411.2738

  39. Levy O, Goldberg Y. Neural word embedding as implicit matrix factorization. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. 2014, 2177–2185

  40. Scarselli F, Gori M, Tsoi A C, Hagenbuchner M, Monfardini G. The graph neural network model. IEEE Transactions on Neural Networks, 2009, 20(1): 61–80

    Article  Google Scholar 

  41. Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations. 2016

  42. Devlin J, Chang M W, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2019

  43. Cui Y, Che W, Liu T, Qin B, Yang Z. Pre-training with whole word masking for Chinese BERT. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 29: 3504–3514

    Article  Google Scholar 

  44. Wang Y, Qiu Z, Jiao Q, Chen C, Meng Z, Cui X. Structure-based protein-drug affinity prediction with spatial attention mechanisms. In: Proceedings of IEEE International Conference on Bioinformatics and Biomedicine. 2021, 92–97

  45. Jiao Q, Qiu Z, Wang Y, Chen C, Yang Z, Cui X. Edge-gated graph neural network for predicting protein-ligand binding affinities. In: Proceedings of IEEE International Conference on Bioinformatics and Biomedicine. 2021, 334–339

  46. Van Laarhoven P J M, Aarts E H L. Simulated annealing. In: Laarhoven P J M, Aarts E H L, eds. Simulated Annealing: Theory and Applications. Dordrecht: Springer, 1987, 7–15

    Chapter  Google Scholar 

  47. Endoh S, Toma N, Yamada K. Immune algorithm for n-TSP. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218). 1998, 3844–3849

  48. Grover A, Leskovec J. node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, 855–864

  49. Chen G, Liu J F, Zheng X Y. Analysis on risk factors and depression status of woman with birth defect child. Chinese Journal of Public Health, 2007, 23(6): 674–675

    Google Scholar 

  50. Murray J, Burgess S, Zuccolo L, Hickman M, Gray R, Lewis S J. Moderate alcohol drinking in pregnancy increases risk for children’s persistent conduct problems: causal effects in a Mendelian randomisation study. Journal of Child Psychology and Psychiatry, 2016, 57(5): 575–584

    Article  Google Scholar 

  51. Martin-Santos R, Egmond E, Cavero M, Marino Z, Subira S, Navines R, Forns X, Valdes M. Chronic hepatitis C, depression and gender: a state of art. Advances in Dual Diagnosis, 2015, 8(4): 193–210

    Article  Google Scholar 

  52. Rao T S S, Prabhakar A K, Rao K S J, Sambamurthy K, Asha M R, Ram D, Nanda A. Relationship between consanguinity and depression in a south Indian population. Indian Journal of Psychiatry, 2009, 51(1): 50–52

    Article  Google Scholar 

  53. Clark A, Skouteris H, Wertheim E H, Paxton S J, Milgrom J. The relationship between depression and body dissatisfaction across pregnancy and the postpartum: a prospective study. Journal of Health Psychology, 2009, 14(1): 27–35

    Article  Google Scholar 

  54. Goyal D, Gay C, Lee K A. How much does low socioeconomic status increase the risk of prenatal and postpartum depressive symptoms in first-time mothers? Women’s Health Issues, 2010, 20(2): 96–104

    Article  Google Scholar 

  55. Li J. Health-related quality of life among pregnant women and related factors in Chinese mainland. Huazhong University of Science and Technology, Dissertation, 2011

  56. Leigh B, Milgrom J. Risk factors for antenatal depression, postnatal depression and parenting stress. BMC Psychiatry, 2008, 8(1): 24

    Article  Google Scholar 

  57. Zachariah R. Social support, life stress, and anxiety as predictors of pregnancy complications in low-income women. Research in Nursing & Health, 2010, 32(4): 391–404

    Article  Google Scholar 

  58. Tsartsara E, Johnson M P. The impact of miscarriage on women’s pregnancy-specific anxiety and feelings of prenatal maternal-fetal attachment during the course of a subsequent pregnancy: An exploratory follow-up study. Journal of Psychosomatic Obstetrics & Gynecology, 2006, 27(3): 173–182

    Article  Google Scholar 

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Acknowledgment

The authors are grateful for the support of the National Key R&D Program of China (No. 2021YFF1201200), the National Natural Science Foundation of China (Grant Nos. 61972174 and 62172187), the Science and Technology Planning Project of Jilin Province (No. 20220201145GX, No. 20200708112YY and No. 20220601112FG), the Science and Technology Planning Project of Guangdong Province (No. 2020A0505100018), Guangdong Universities’ Innovation Team Project (No. 2021KCXTD015) and Guangdong Key Disciplines Project (No. 2021ZDJS138).

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Authors and Affiliations

  1. College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Xiaosong Han, Mengchen Cao, Xiaoyue Feng & Renchu Guan

  2. Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA

    Dong Xu

  3. Department of Computer Science and Technology, Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Science and Technology, Zhuhai, 519041, China

    Yanchun Liang

  4. Jilin Provincial Institute of Population Science and Technology, Changchun, 130000, China

    Xiaoduo Lang

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  1. Xiaosong Han
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Correspondence to Renchu Guan.

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Competing interests The authors declare that they have no competing nterests or financial conflicts to disclose.

Additional information

Xiaosong Han got his PhD degree from Jilin University, China in 2012. The research fields are machine learning and bioinformatics.

Mengchen Cao got a bachelor’s degree from Jilin University, China in 2017. The research field is machine learning.

Dong Xu received his PhD degree from the University of Illinois at Urbana-Champaign, USA. The research fields are computational biology and bioinformatics.

Xiaoyue Feng got her PhD degree from Jilin University, China. The research fields are knowledge graph and recommendation system.

Yanchun Liang got his PhD degree from Jilin University, China in 1997. The research fields are machine learning and computational intelligence.

Xiaoduo Lang works in Jilin Provincial Institute of Population Science and Technology, China. The research fields are obstetrics and gynecology.

Renchu Guan got his PhD degree from Jilin University, China. The research fields are knowledge engineering and bioinformatics.

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Han, X., Cao, M., Xu, D. et al. SEOE: an option graph based semantically embedding method for prenatal depression detection. Front. Comput. Sci. 18, 186911 (2024). https://doi.org/10.1007/s11704-024-3612-4

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