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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (6): 99-108    DOI: 10.11925/infotech.2096-3467.2018.0824
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Deep Neural Network Learning for Medical Triage
Kan Liu(),Lu Chen
School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
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[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.

Key wordsMedical Triage      Electronic Medical Records      Convolutional Neural Network      Pre-training     
Received: 25 July 2018      Published: 15 August 2019

Cite this article:

Kan Liu,Lu Chen. Deep Neural Network Learning for Medical Triage. Data Analysis and Knowledge Discovery, 2019, 3(6): 99-108.

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