Selecting appropriate consultations for self-referred patients to tertiary medical centers is a time and resource intensive task. Deep learning with natural language processing can potentially augment this task and reduce clinician workload. Appointment request forms for 8168 patients self-referred to General Internal Medicine were reviewed and recommended downstream appointments from manual triage were tabulated. This paper describes the development and performance of thirty-nine deep learning algorithms for multi-label text classification: including convolutional neural networks, recurrent neural networks, and pretrained language models with transformer and reformer architectures implemented using Pytorch and trained on a single graphic processing unit. A model with multiple convolutional neural networks with various kernel sizes (1-7 words) and 300 dimensional FastText word embeddings performed best (AUC 0.949, MCC 0.734, F1 0.775). Generally, models with convolutional networks were highest performers. Highly performing models may be candidates for implementation to augment clinician workflow.
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