What type of study is this?

This is a Prospective Cohort Study study.

Development and Validation of a Machine Learning‐Based Risk Prediction Model for Delirium in Older Inpatients: A Prospective Cohort Study

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Prospective cohort study demonstrates high accuracy and sensitivity in predicting delirium risks, indicating potential for enhanced patient care.

The machine learning model achieves an AUC range of 0.869 to 0.992 in predicting delirium risk.
Random Forest outperformed other models with a sensitivity of 0.992 and an accuracy of 0.935.
Eighteen clinical features were collected to inform the risk prediction model's performance metrics.
Early identification of patients at risk of delirium may enable timely interventions and reduce severity.

Zhou et al. (2025) studied this question.

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Prospective cohort study demonstrates high accuracy and sensitivity in predicting delirium risks, indicating potential for enhanced patient care.