Delirium screening in an acute care setting with a machine learning classifier based on routinely collected nursing data: A model development study

Spiller, Tobias R; Tufan, Ege; Petry, Heidi; Böttger, Sönke; Fuchs, Simon; Duek, Or; Ben-Zion, Ziv; Korem, Nachshon; Harpaz-Rotem, Ilan; von Känel, Roland; Ernst, Jutta

doi:10.1016/j.jpsychires.2022.10.018

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Spiller, T. R., Tufan, E., Petry, H., Böttger, S., Fuchs, S., Duek, O., Ben-Zion, Z., Korem, N., Harpaz-Rotem, I., von Känel, R., & Ernst, J. (2022). Delirium screening in an acute care setting with a machine learning classifier based on routinely collected nursing data: A model development study. Journal of Psychiatric Research, 156, 194–199. https://doi.org/10.1016/j.jpsychires.2022.10.018

Abstract

Delirium screening in acute care settings is a resource intensive process with frequent deviations from screening protocols. A predictive model relying only on daily collected nursing data for delirium screening could expand the populations covered by such screening programs. Here, we present the results of the development and validation of a series of machine-learning based delirium prediction models. For this purpose, we used data of all patients 18 years or older which were hospitalized for more than a day between January 1, 2014,