Doctor Recommendation Model for Pre-Diagnosis Online in China: Integrating Ontology Characteristics and Disease Text Mining (Preprint)

BACKGROUND Background: The online health community provides diagnosis and treatment assistance online so that doctors and patients can keep in touch continuously anytime and anywhere. Specifically, patients can access medical services such as disease diagnosis online, medical treatment guidance, medication guidance, etc. 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 non-acute or malignant diseases, and hence there may be offline medical treatment. Therefore, this paper proposes an online pre-diagnosis doctor recommendation model by integrating ontology characteristics and disease text. Particularly, this recommendation model takes full consideration of geographical location of patients. OBJECTIVE 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 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 were 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’ pre-diagnosis 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 Results: In the online medical field, compared with 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 Conclusions: The proposed online pre-diagnosis 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 pre-diagnosis doctor recommendation model would improve patients’ online consultation experience and offline treatment convenience, enriching the value of online pre-diagnosis data.