Characteristics, utilization of reproductive health services and AI prediction among Taiwanese adolescent mothers during the COVID-19 pandemic

Background: Although adolescent birth rates have declined globally, the sexual and reproductive health of adolescent mothers remains an area of specific concern, and these were impacted by the COVID-19 pandemic. This study investigates characteristics, utilization of reproductive health services (RHS) and artificial intelligence (AI) prediction during the pandemic.

Methods: We conducted an exploratory study using data for 2020-2022 from the Taipei City Government Health Bureau. Adolescent mothers under the age of 20 received post-birth telephone-based RHS, covering contraception, abortion, postpartum care, and social welfare support. The data analysis included descriptive statistics, and various machine learning techniques were employed, including random forest, SVM, KNN, logistic regression, and Bayesian network analysis.

Results: Of 112 participants, most were aged 17 to 19 (80.4%) and married (58.0%). The majority had full-term deliveries (86.6%) with healthy infants. A high percentage had not used contraception before conception (60.7%), and some had had earlier abortion or termination experiences (13.4%). In the examination of eight influential factors, the machine learning models, specifically the random forest and Bayesian network analyses, exhibited the highest accuracy, achieving 90.91% and 89%, respectively, in predicting service acceptance. The key determinants identified were abortion experience and marital status, directly influencing the acceptance of services.

Conclusion: The COVID-19 pandemic reduced hospital visits for adolescent mothers, but the RHS provided timely guidance. Telemedicine consultations and internet-based psychological consultations may play a crucial role in facilitating such services in the future.