BackgroundKey opinion leaders (KOLs) are people who can influence public opinion on a certain subject matter. In the field of medical and health informatics, it is critical to identify KOLs on various disease conditions. However, there have been very few studies on this topic.ObjectiveWe aimed to develop a recommender system for identifying KOLs for any specific disease with health care data mining.MethodsWe exploited an unsupervised aggregation approach for integrating various ranking features to identify doctors who have the potential to be KOLs on a range of diseases. We introduce the design, implementation, and deployment details of the recommender system. This system collects the professional footprints of doctors, such as papers in scientific journals, presentation activities, patient advocacy, and media exposure, and uses them as ranking features to identify KOLs.ResultsWe collected the information of 2,381,750 doctors in China from 3,657,797 medical journal papers they published, together with their profiles, academic publications, and funding. The empirical results demonstrated that our system outperformed several benchmark systems by a significant margin. Moreover, we conducted a case study in a real-world system to verify the applicability of our proposed method.ConclusionsOur results show that doctors’ profiles and their academic publications are key data sources for identifying KOLs in the field of medical and health informatics. Moreover, we deployed the recommender system and applied the data service to a recommender system of the China-based Internet technology company NetEase. Patients can obtain authority ranking lists of doctors with this system on any given disease.
With the explosive development of communication technologies, more customer friendly services have been designed for the next generation of cellular technology, that is, fifth-generation (5G) communication. However, such services require more computing resources and energy. Thus, the development of green and energy-efficient 5G application systems has become an important topic in communications. In this paper, we focus on high-performance multi-label classification methods and their application for medical recommendations in the domain of 5G communication. In machine learning, multi-label classification involves assigning multiple target labels to each query instance. The vast number of labels poses a challenge for maintaining efficiency. Several related approaches have been proposed to meet this challenge. In this paper, we propose two label selection methods for multi-label classification: clustering-based sampling and frequency-based sampling. We apply our proposed multi-label classification methods as an innovative 5G application to predict doctor labels for doctor recommendations. We perform experiments on real-world data sets. The experimental results show that our methods achieve the state-of-the-art performance compared with baselines. In addition, we develop a mobile application of a doctor recommendation system based on our proposed methods.
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