65. The simulation of mental health nursing scenarios based on generative adversarial networks

Abstract Background Mental health care is a crucial topic in the current medical field. With the increasing severity of mental health issues, the demand for professional nursing personnel is surging, and improving the practical skills of nurses relies heavily on scenario simulation training. However, traditional scenario simulations suffer from problems such as severe scenario homogenization, insufficient personalization, and high costs, making it difficult to cover the diverse nursing scenarios for different mental illnesses. Generative Adversarial Networks (GANs) have emerged as promising technologies in fields such as medical image generation and clinical simulation due to their powerful generative capabilities. Existing research has confirmed their ability to construct highly realistic medical scenarios, but research on their application in mental health care scenario generation is still in its early stages. This study constructs a GAN-based mental health care scenario simulation system, aiming to provide new insights for improving the practical skills of nurses. Methods Using 1000 nursing cases of psychological disorders as the dataset, an improved GAN system is constructed. The generator adopts the U-Net network structure and takes core information such as patient age, disease type, and symptom severity as inputs to output dynamic nursing scenario scripts and virtual interactive scenarios. The discriminator uses convolutional neural networks to distinguish the similarity between generated scenarios and real clinical cases. Recruit 80 students majoring in mental health nursing as research subjects, and use a random number table method to divide the subjects into an experimental group and a control group, with 40 people in each group. The control group received traditional standardized training, while the experimental group received GAN personalized scenario training. The intervention period was 4 weeks, with 1 hour of training per day. The subjects were evaluated from dimensions such as nursing skills operation, communication effectiveness, and emergency response ability. Results The nursing skill operation score of the experimental group was 89.23 ± 5.12 points, while the nursing skill operation score of the control group was 76.58 ± 6.34 points. The experimental group was higher than the control group, and the difference between the two groups was statistically significant (p<.05). The communication effectiveness scores of the experimental group and the control group were 90.15 ± 4.86 points and 75.32 ± 5.98 points, respectively. The experimental group was significantly higher than the control group, and the difference between the two groups was significant (p<.05). The emergency response capability score of the experimental group was 87.69 ± 5.34 points, which was higher than the control group's 73.85 ± 6.12 points, and the difference was statistically significant (p<.05). Discussion The mental health nursing scenario simulation system based on GAN can significantly reduce training costs and resource investment, providing new technical support for the cultivation of mental health nursing talents. In the future, the training dataset will be further expanded to include mental health care cases for special populations such as children and the elderly, in order to enhance the comprehensiveness of the system's scenario coverage.