The Role of Neonatal Nosocomial Infection towards Health Behavior: A Multivariate Analysis and Deep Learning Approach of Informationized Health Management

Abstract

Objectives: In this study, we investigated the impact of neonatal nosocomial infections on health behavior intentions using a multivariate analysis and deep learning approach within the framework of informationized health management. Methods: We analyzed the data of 155 neonates in our hospital from May 2019 to May 2020. The infected newborns were divided into an experimental and a control group, infection risk factors and their impact on health behavioral intentions were explored, and neonates were given informationized health management based on deep learning. Results: We have analyzed risk factors, infected parts of the respondents, adverse events before and after the intervention of the experimental group, and Apgar scores before and after the intervention of the experimental group. The parts of neonatal nosocomial infection were mainly in the respiratory, urinary, and digestive tracts which significantly impacted the formulation of health behaviors. The adverse events in the experimental group after the intervention was significantly lower than before (p < .05). Meanwhile, the mean Apgar score of the experimental group after the intervention was significantly higher than before (p < .001). Conclusion: Many high-risk factors for neonatal nosocomial infections significantly impact health behavior. Adopting informationized health management based on deep learning can reduce infection and improve neonates′ health.