This study by Chang et al. investigates the factors associated with anxiety in patients receiving treatment for COVID-19. This investigation is done via a random forest machine learning model. A random forest machine learning model is made up of many decision trees, where each tree is trained on a random subset of data, and when making a decision each tree gives its “vote” and the forest takes the majority vote for classification or the average for regression. Researchers collected data on patient demographics, clinical characteristics, treatment regimens, and psychological variables. The random forest tree algorithm was applied to specific predictors of anxiety, combining key factors such as treatment side effects, duration of illness, comorbid conditions, and levels of social support. The study was able to handle complex non-linear relationships and interactions among variables, providing a more accurate prediction than traditional statistical methods. The results suggest that incorporating predictive modeling into patient monitoring could allow healthcare providers to identify high-risk individuals earlier and implement targeted psychological and supportive interventions.