Trends in Labor Analgesia: Analysis of Patients’ Web Searches Across Europe Using a Machine Learning Model
Leoni MLG, Rekatsina M, Varrassi G, Cascella M, Pasqualucci A, Sansone P, Todde C, Caruso A, & Mercieri M. (2025). Trends in Labor Analgesia: Analysis of Patients’ Web Searches Across Europe Using a Machine Learning Model. Preprints. https://doi.org/10.20944/preprints202501.0543.v1
Abstract
Epidural analgesia is widely regarded as the gold standard for pain relief during labor. Despite its effectiveness, significant disparities in adoption persist due to cultural, medical, and informational factors. This study aimed to analyze online search behaviors related to epidural analgesia in the six most populous European countries, evaluate temporal trends, and assess the predictive power of machine learning models for search volumes.MethodsWeekly search data from 2020 to 2024 were obtained from Google Trends for France, Germany, Italy, Spain, Turkey, and the United Kingdom (UK). Data were analyzed using linear regression, time-series decomposition, and Mann-Kendall tests to identify monotonic trends. An Auto Regressive Integrated Moving Average (ARIMA) model was developed to forecast search volumes for 2025. Machine learning models such as Random Forest (RF) and Gradient Boosting Machine (GBM), were employed to evaluate the influence of variables such as country and temporal factors on search patterns. Model performance was assessed using specific metric (R², RMSE, MAE, and MBE) and statistical comparisons were made between the models.ResultsFrance and Turkey exhibited significant downward trends in search interest, while Germany showed a slight upward trend, and Italy, Spain, and the UK demonstrated stable patterns. ARIMA forecast indicated stable search volumes for most countries, with the UK reaching the highest activity. RF outperformed GBM, achieving R² values of 0.92 (testing) and 0.93 (training), with "Country" identified as the most influential predictor. Associated queries highlighted common public concerns, including epidural timing, risks, and side effects.ConclusionsThese findings reveal the value of understanding public interest in epidural analgesia to address concerns effectively. Healthcare providers should guide patients toward reliable online information. Future initiatives should include educational tools, national health programs, and interdisciplinary collaboration to enhance informed decision-making and optimize maternal care outcomes.
