Assessing artificial intelligence ability in predicting hospitalization duration for pleural empyema patients managed with uniportal video-assisted thoracoscopic surgery: a retrospective observational study.

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.