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Advanced natural language processing technique to predict patient disposition based on emergency triage notes
Author(s) -
Tahayori Bahman,
ChiniForoush Noushin,
Akhlaghi Hamed
Publication year - 2021
Publication title -
emergency medicine australasia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.602
H-Index - 52
eISSN - 1742-6723
pISSN - 1742-6731
DOI - 10.1111/1742-6723.13656
Subject(s) - triage , medicine , artificial intelligence , machine learning , emergency department , disposition , natural language processing , retrospective cohort study , medical emergency , computer science , nursing , psychology , social psychology
Objective To 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. Methods A 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. Results The 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. Conclusion Machine 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.