How to find your appropriate doctor: An integrated recommendation framework in big data context

To find a specialty-counterpart, diagnosis-accurate, skill-superb, reputation-high, and meanwhile cost-effective and distance-close doctor is always essential for patients but not an easy job. According to various categories of medical professions, the diversity of user symptoms, and the information asymmetry and incompetence of doctors' profiles as well as patients' medical history, today most recommender applications are difficult to fit this field. The emerging web medical databases and online communities, providing doctors information and user reviews for them respectively, make it possible to personalized medical recommender services. In this paper, we describe an integrated recommender framework for seeking doctors in accordance with patients' demand characteristics, including their illness symptoms and their preference. In the proposed method, a users' matching model is firstly suggested for finding the similarities between users' consultation and doctors' profiles. Second, to measure doctors' quality, doctors' experiences and dynamic user's opinions are considered. Finally, to combine the results of the relevance model and the quality model, an AHP based integrated method is suggested for doctor recommendation. A mobile recommender APP is proposed to demonstrate the framework as above. And a survey is carried out for method evaluation. The results illustrate the new recommender outperforms others on accuracy and efficiency, as well as user experience. Our paper provides an efficient method for doctor recommendation, which has good practical value in China regarding to its huge land area with medical resource's uneven distribution.