[Objective] This paper designs and implements an unsupervised algorithm to evaluate the information accuracy of physicians’ feedbacks from online consulting service. [Methods] First, we identified word co-occurrence relationships based on large amount of online service records. Then, we built a statistical model to predict standard feedbacks for the given questions. Finally, we decided the accuracy of physicians’ answers by calculating content similarity between real feedbacks and the standard ones. [Results] We examined the proposed algorithm with records from Haodf.com as well as manually labeled results. The accuracy rates were 41.0% and 82.4% for rigorous and relax matching. [Limitations] We did not include the word sequence information in the algorithm. [Conclusions] The proposed algorithm could help patients know the accuracy of online medical information and improve their healthcare decisions makings.
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Liu Tong,Yang Jingcheng. Evaluating Online Healthcare Consultation Feedbacks Based on Signal Transmission Algorithm. Data Analysis and Knowledge Discovery, 2017, 1(11): 29-36.
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