External Validation of a Machine Learning Model for Delirium Risk

In this quality improvement study, an ML-based delirium risk stratification model maintained acceptable discriminative performance during external deployment without retraining and was similar to its performance at the development site without retraining. Model deployment was also associated with increased diagnostic yield but a high false positive rate. These findings provide further evidence of the potential generalizability of the model.

Corresponding Author: Joseph I. Friedman, MD, Department of Psychiatry, Mount Sinai Hospital, One Gustave L. Levy Pl, Box 1230, New York, NY 10029 (joseph.friedman@mountsinai.org; Jfriedman1@rcn.com).

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