RESUMO
BACKGROUND: Patients can access medical services such as disease diagnosis online, medical treatment guidance, and medication guidance that are provided by doctors from all over the country at home. Due to the complexity of scenarios applying medical services online and the necessity of professionalism of knowledge, the traditional recommendation methods in the medical field are confronting with problems such as low computational efficiency and poor effectiveness. At the same time, patients consulting online come from all sides, and most of them suffer from nonacute or malignant diseases, and hence, there may be offline medical treatment. Therefore, this paper proposes an online prediagnosis doctor recommendation model by integrating ontology characteristics and disease text. Particularly, this recommendation model takes full consideration of geographical location of patients. OBJECTIVE: The recommendation model takes the real consultation data from online as the research object, fully testifying its effectiveness. Specifically, this model would make recommendation to patients on department and doctors based on patients' information of symptoms, diagnosis, and geographical location, as well as doctor's specialty and their department. METHODS: Utilizing crawler technique, five hospital departments were selected from the online medical service platform. The names of the departments were in accordance with the standardized department names used in real hospitals (e.g., endocrinology, dermatology, gynemetrics, pediatrics, and neurology). As a result, a dataset consisting of 20000 consultation questions by patients was built. Through the application of Python and MySQL algorithms, replacing semantic dictionary retrieval or word frequency statistics, word vectors were utilized to measure similarity between patients' prediagnosis and doctors' specialty, forming a recommendation framework on medical departments or doctors based on the above-obtained sentence similarity measurement and providing recommendation advices on intentional departments and doctors. RESULTS: In the online medical field, compared with the traditional recommendation method, the model proposed in the paper is of higher recommendation accuracy and feasibility in terms of department and doctor recommendation effectiveness. CONCLUSIONS: The proposed online prediagnosis doctor recommendation model integrates ontology characteristics and disease text mining. The model gives a relatively more accurate recommendation advice based on ontology characteristics such as patients' description texts and doctors' specialties. Furthermore, the model also gives full consideration on patients' location factors. As a result, the proposed online prediagnosis doctor recommendation model would improve patients' online consultation experience and offline treatment convenience, enriching the value of online prediagnosis data.