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Mixed-data deep learning in repeated predictions of general medicine length of stay: a derivation study

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The accurate prediction of likely discharges and estimates of length of stay (LOS) aid in effective hospital administration and help to prevent access block. Machine learning (ML) may be able to help with these tasks. For consecutive patients admitted under General Medicine at the Royal Adelaide Hospital over an 8-month period, daily ward round notes and relevant discrete data fields were collected from the electronic medical record. These data were then split into training and testing sets (7-month/1-month train/test split) prior to use in ML analyses aiming to predict discharge within the next 2 days, discharge within the next 7 days and an estimated date of discharge (EDD). Artificial neural networks and logistic regression were effective at predicting discharge within 48 h of a given ward round note. These models achieved an area under the receiver operator curve (AUC) of 0.80 and 0.78, respectively. Prediction of discharge within 7 days of a given note was less accurate, with artificial neural network returning an AUC of 0.68 and logistic regression an AUC of 0.61. The generation of an exact EDD remains inaccurate. This study has shown that repeated estimates of LOS using daily ward round notes and mixed-data inputs are effective in the prediction of general medicine discharges in the next 48 h. Further research may seek to prospectively and externally validate models for prediction of upcoming discharge, as well as combination human-ML approaches for generating EDDs.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Correspondence to Stephen Bacchi.

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Ethics approval was granted for this project by the Central Adelaide Local Health Network Research Ethics Committee (HREC/19/CALHN/209).

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Bacchi, S., Gluck, S., Tan, Y. et al. Mixed-data deep learning in repeated predictions of general medicine length of stay: a derivation study. Intern Emerg Med 16, 1613–1617 (2021).

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