[Objective] This paper proposes an algorithm to accurately assign specialists for outpatients based on their major complaints and medical histories. [Methods] We applied the convolutional neural network model to classify the medical short texts, and learn the correlation between medical terms, which were the tasks for pre-training. Then, we examined the structure, parameters and weights of the pre-trained model with actual texts of main complaint and medical history. Finally, we modified the network to obtain the final learning outcome. [Results] The F-score of the proposed approach reached 88% with the sample dataset, which was 6% higher than that of the current best baseline model. The pre-trained model significantly improved the training efficiency. [Limitations] We did not directly work with the patient’s actual complaints at the triage desk. We only used their electronic medical records, which might yield inaccurate results. [Conclusions] The proposed triage model improves the efficiency of medical triage, and promote precision medical treatment for patients.
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