• Stephen Bacchi, Luke Oakden-Rayner, Toby Zerner, Timothy Kleinig, Sandy Patel, and Jim Jannes.
• From the Royal Adelaide Hospital, Adelaide, Australia (S.B., L.O.-R., T.K., S.P., J.J.).
• Stroke. 2019 Mar 1; 50 (3): 758-760.

AbstractBackground and Purpose- Triaging of referrals to transient ischemic attack (TIA) clinics is aided by risk stratification. Deep learning-based natural language processing, a type of machine learning, may be able to assist with the prediction of cerebrovascular cause of TIA-like presentations from free-text information. Methods- Consecutive TIA clinic notes were retrieved from existing databases. Texts associated with cerebrovascular and noncerebrovascular diagnoses were preprocessed before classification experiments, using a variety of classifier models, based on only the free-text description of the history of presenting complaint. The primary outcome was area under the curve (AUC) of the receiver operator curve. The model with the greatest AUC was then used in classification experiments in which it was provided with additional clinical information. Results- Of the classifier models trialed on the history of presenting complaint, the convolutional neural network achieved the greatest predictive capability (AUC±SD; 81.9±2.0). The effects of additional clinical information on AUC were variable. The greatest AUC was achieved when the convolutional neural network was provided with the history of presenting complaint and magnetic resonance imaging report (88.3±3.6). Conclusions- Deep learning-based natural language processing, in particular convolutional neural networks, based on medical free-text, may prove effective in prediction of the cause of TIA-like presentations. Future research investigating the role of the application of deep learning-based natural language processing to the automated triaging of clinic referrals in TIA, and potentially other specialty areas, is indicated.

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