{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:38:27Z","timestamp":1785544707067,"version":"3.56.0"},"reference-count":39,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Online health care consultation provides patients with broad access to physicians, but also presents the challenge of selecting a suitable doctor in the absence of triage guidance. Patients have multifaceted needs, prioritizing not only recommendation accuracy but also physician service quality, and diversity of physician expertise. Furthermore, the sparsity of patient interaction data intensifies the difficulty of providing balanced and effective recommendations.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to develop a multiobjective physician recommendation method that simultaneously optimizes recommendation accuracy, service quality, and diversity of physician expertise while addressing the data sparsity challenge inherent in online health care platforms.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We propose dynamic graph\u2013based bacteria colony optimization for multiobjective physician recommendation (DyGMO-PR), a dynamic graph\u2013based multiobjective optimization method. It integrates bacterial colony optimization with an evolving physician relationship graph. Our approach features a novel graph\u2013based encoding scheme, a chemotaxis-inspired graph-walking strategy for stable search, and a dynamic graph evolution mechanism that learns implicit physician relationships to enhance recommendation quality under sparse data conditions.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Evaluation on a real-world dataset comprising 10,493 consultation records from 1256 patients and 1377 physicians demonstrates the effectiveness of DyGMO-PR. Our method consistently outperforms 6 state-of-the-art multiobjective recommendation algorithms. Using a recommendation list length of 6 as an example, DyGMO-PR achieves an accuracy of 0.876, a service quality of 0.873, and a diversity of 0.720, surpassing the best-performing baseline by 7.4%, 3.1%, and 8.1%, respectively. DyGMO-PR also attains the highest hypervolume value (0.696 at\n K<\/jats:italic>\n =6) and the highest recall (0.380 at\n K<\/jats:italic>\n =6). Case studies and graph analysis further demonstrate the method\u2019s effectiveness in generating interpretable and balanced recommendation lists.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n DyGMO-PR offers an effective solution for multiobjective physician recommendation. 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