Delirium is an acute decline in cognitive function leading to confusion, which occurs in 29%–65% of hospitalized elderly patients. Previous studies have applied machine learning to predict delirium; however, existing models do not account for temporal data. We propose a method to capture temporal correlations using an LSTM-based model to predict new-onset delirium. We extracted data for all adult patients who had a CAM assessment between January 1, 2018 and October 1, 2021 at Vanderbilt University Medical Center. We developed a deep learning model with 2 parts: a fixed-length LSTM-based model and a machine learning model. We compared its performance with machine-learning-only models (logistic regression, random forest, support vector machine, neural network, and LightGBM). We calculated SHapley Additive exPlanations to gauge the feature impact. A total of 331,489 records from 34,035 patients (896 features) were included. The LSTM-based deep learning model achieved an AUC of 0.952 [0.950, 0.955] and F1 of 0.759 [0.755, 0.765], which showed a significant improvement compared to using machine learning only (p=.001). Leveraging LSTM to develop a deep learning model to capture temporal trends can significantly improve the prediction of new-onset delirium, providing an algorithmic basis for the subsequent development of clinical decision support tools for proactive delirium interventions.