%A Ruojia Wang,Lu Zhang,Jimin Wang %T Automatic Triage of Online Doctor Services Based on Machine Learning %0 Journal Article %D 2019 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2019.0147 %P 88-97 %V 3 %N 9 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4712.shtml} %8 2019-09-25 %X

[Objective] This paper compares the performance of various machine learning algorithms for automatic triage, aiming to improve their effectiveness through analyzing mis-classification data. [Methods] First, we retrieved 33,073 real patients’ questions from a website named “chunyu doctor”. Then, we compared the accuracy of two text vectorization methods and six classification models. Finally, we analyzed the mis-classification data and extracted new features to improve the performance of models. [Results] The best automatic triage model used TF-IDF as text vectorization method and support vector machine as classification algorithm. After adding age and gender characteristics, the classification accuracy rate reached 76.3%. The classifier had the lowest accuracy rate for surgery department due to the setting of this platform’s categories. [Limitations] We assumed that the department selection of the patient was correct. [Conclusions] Machine learning techniques could improve the performance of automatic triage services of the online health consulting platforms.