Assessing artificial intelligence ability in predicting hospitalization duration for pleural empyema patients managed with uniportal video-assisted thoracoscopic surgery: a retrospective observational study - Issa Alnajjar; Baraa Alshakarnah; Tasneem AbuShaikha; Tareq Jarrar; Abed Al-Raheem Ozrail; Yousef Abu Asbeh (BMC Surgery)
Abstract Background This retrospective observational research evaluates the potential applicability of artificial intelligence models to predict the length of hospital stay for patients with pleural empyema who underwent uniportal video-assisted thoracoscopic surgery. Methods Data from 56 patients were analyzed using two artificial intelligence models. A Random Forest Regressor, the initial model, was trained using clinical data unique to each patient. Weighted factors from evidence-based research were incorporated into the second model, which was created using a prediction approach informed by the literature. Results The two models tested showed poor prediction accuracy. The first one had a mean absolute error of 4.56 days and a negative R2 value. The literature-informed model performed similarly, with a mean absolute error of 4.53 days and an R2 below zero. Conclusions While artificial intelligence holds promise in supporting clinical decision-making, this study demonstrates the challenges of predicting length of stay in pleural empyema patients due to significant clinical variability and the current limitations of AI-based models. Future research should focus on integrating larger, multi-center datasets and more advanced machine learning approaches to enhance predictive accuracy.