Daily estimates of individual discharge likelihood with deep learning natural language processing in general medicine: a prospective and external validation study

Stephen Bacchi, Toby Gilbert, Samuel Gluck, Joy Cheng, Yiran Tan, Ivana Chim, Jim Jannes, Timothy Kleinig, Simon Koblar

Research output: Contribution to journalArticlepeer-review

Abstract

Machine 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.

Original languageEnglish
Pages (from-to)411-415
Number of pages5
JournalInternal and Emergency Medicine
Volume17
Issue number2
DOIs
Publication statusPublished or Issued - Mar 2022

Keywords

  • Artificial intelligence
  • Length of stay
  • Natural language processing
  • Neural network

ASJC Scopus subject areas

  • Internal Medicine
  • Emergency Medicine

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