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Automatic Triage of Online Doctor Services Based on Machine Learning |
Ruojia Wang1,2,Lu Zhang1,Jimin Wang1() |
1 Department of Information Management, Peking University, Beijing 100871, China 2 Institute of Ocean Research, Peking University, Beijing 100871, China |
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Abstract [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.
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Received: 11 February 2019
Published: 23 October 2019
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