Individual Doctor Recommendation Model on Medical Social Network

Abstract

It is difficult for patients to find the most appropriate doctor/physician to diagnose. In most cases, just considering Authority Degrees of Candidate Doctors(AD-CDs) cannot satisfy this need due to some objective preferences such as economic affordability of a patient, commuting distance for visiting doctors and so on. In this paper, we try to systematically investigate the problem and propose a novel method to enable patients access such intelligent medical service like this. In the method, we first mine patient-doctor relationships via Time-constraint Probability Factor Graph mode(TPFG) from a medical social network, and then extract four essential features for AD-CDs that would be subsequently sorted via Ranking SVM. At last, combining AD-CDs and patients’ preferences together, we propose a novel Individual Doctor Recommendation Model, namely IDR-Model, to compute doctor recommendation success rate based on weighted average method. We conduct experiments to verify the method on a real medical data set. Experimental results show that we obtain the better accuracies of mining patient-doctor relationship from the network, AD-CDs ranking is also better than the traditional Reduced SVM method, and doctor recommendation results of IDR-Model is reasonable and satisfactory.