• Bahman Tahayori, Noushin Chini-Foroush, and Hamed Akhlaghi.
• Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.
• Emerg Med Australas. 2021 Jun 1; 33 (3): 480484480-484.

ObjectiveTo demonstrate the potential of machine learning and capability of natural language processing (NLP) to predict disposition of patients based on triage notes in the ED.MethodsA retrospective cohort of ED triage notes from St Vincent's Hospital (Melbourne) was used to develop a deep-learning algorithm that predicts patient disposition. Bidirectional Encoder Representations from Transformers, a recent language representation model developed by Google, was utilised for NLP. Eighty percent of the dataset was used for training the model and 20% was used to test the algorithm performance. Ktrain library, a wrapper for TensorFlow Keras, was employed to develop the model.ResultsThe accuracy of the algorithm was 83% and the area under the curve was 0.88. Sensitivity, specificity, precision and F1-score of the algorithm were 72%, 86%, 56% and 63%, respectively.ConclusionMachine learning and NLP can be together applied to the ED triage note to predict patient disposition with a high level of accuracy. The algorithm can potentially assist ED clinicians in early identification of patients requiring admission by mitigating the cognitive load, thus optimises resource allocation in EDs.© 2020 Australasian College for Emergency Medicine.

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