• Stephen Bacchi, Toby Gilbert, Samuel Gluck, Joy Cheng, Yiran Tan, Ivana Chim, Jim Jannes, Timothy Kleinig, and Simon Koblar.
• Royal Adelaide Hospital, Port Road, Adelaide, SA, 5000, Australia. stephen.bacchi@sa.gov.au.
• Intern Emerg Med. 2022 Mar 1; 17 (2): 411-415.

AbstractMachine learning, in particular deep learning, may be able to assist in the prediction of the length of stay and timing of discharge for individual patients. Artificial neural networks applied to medical text have previously shown promise in this area. In this study, a previously derived artificial neural network was applied to prospective and external validation datasets. In the prediction of discharge within the next 2 days, when the algorithm was applied to prospective and external datasets, the area under the receiver operator curve for this task were 0.78 and 0.74, respectively. The performance in the prediction of discharge within the next 7 days was more limited (area under the receiver operator curve 0.68 and 0.67). This study has shown that in prospective and external validation datasets the previously derived deep learning algorithms have demonstrated moderate performance in the prediction of which patients will be discharged within the next 2 days. Future studies may seek to further refine or evaluate the effect of the implementation of such algorithms.© 2021. Crown.

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