title: Multiview Physician-Specific Attributes Fusion for Health Seeking.
abstract: Community-based health services have risen as important online resources for resolving users health concerns. Despite the value, the gap between what health seekers with specific health needs and what busy physicians with specific attitudes and expertise can offer is being widened. To bridge this gap, we present a question routing scheme that is able to connect health seekers to the right physicians. In this scheme, we first bridge the expertise matching gap via a probabilistic fusion of the physician-expertise distribution and the expertise-question distribution. The distributions are calculated by hypergraph-based learning and kernel density estimation. We then measure physicians attitudes toward answering general questions from the perspectives of activity, responsibility, reputation, and willingness. At last, we adaptively fuse the expertise modeling and attitude modeling by considering the personal needs of the health seekers. Extensive experiments have been conducted on a real-world dataset to validate our proposed scheme.
authors.forename: Liqiang
authors.initials: L
authors.lastname: Nie
authors.forename: Luming
authors.initials: L
authors.lastname: Zhang
authors.forename: Yan
authors.initials: Y
authors.lastname: Yan
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Chang
authors.forename: Maofu
authors.initials: M
authors.lastname: Liu
authors.forename: Ling
authors.initials: L
authors.lastname: Shaoling
mesh_terms: D000465:Algorithms; D001291:Attitude of Health Personnel; D003153:Community Health Services; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data*; D006801:Humans; D000069550:Machine Learning; D008490:Medical Informatics / Q000379:methods*; D010342:Patient Acceptance of Health Care; D010820:Physicians / Q000706:statistics & numerical data*