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.
To achieve these, a research team led by Xiaosong Han published their new research on 15 December 2024 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.
The team proposed a prenatal depression detection method using the semantically enhanced option embedding (SEOE) model to represent questionnaire options. It can quantitatively determine the relationship and patterns between options and depression.
In the research, 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, the author compared it with other deep learning and traditional machine learning methods. The experiment results show that the proposed model can accurately identify pregnant women with depression and reach an F1 score of 0.8. In addition, the model is of low computational complexity and strong generalization, which can be widely applied to other questionnaire analyses of psychiatric disorders.
DOI: 10.1007/s11704-024-3612-4
Frontiers of Computer Science
Experimental study
Not applicable
SEOE: an option graph based semantically embedding method for prenatal depression detection
15-Dec-2024