Advanced natural language processing technique to predict patient disposition based on emergency triage notes.

ABSTRACT

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.