New onset delirium prediction using machine learning and long short-term memory (LSTM) in electronic health record.

Leveraging LSTM to capture temporal trends and combining it with the LightGBM model 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.