Advanced natural language processing technique to predict patient disposition based on emergency triage notes
Bahman Tahayori, Noushin Chini-Foroush, Hamed Akhlaghi
Emergency Medicine Australasia | WILEY | Published : 2020
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 ..View full abstract
This project received a research endowment fund from St Vincent's research department.