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A Dynamic Graph-Based Multiobjective Optimization Method for Physician Recommendation: Development and Evaluation
Shuang Geng1, Rui Wang1, Wenli Zhang2
1Deparment of Management Science, College of Management, Shenzhen University, 1066 Xueyuan Road, Shenzhen, 518000, China, 86 852 2653 4283.
JMIR Medical Informatics
|July 31, 2026
Summary
This study introduces DyGMO-PR, a novel method for multiobjective physician recommendation that balances accuracy, service quality, and expertise diversity. It effectively addresses data sparsity in online healthcare platforms, improving patient-doctor matching.
Area of Science:
- Health Informatics
- Artificial Intelligence
- Optimization Algorithms
Background:
- Online healthcare platforms offer broad physician access but lack effective doctor selection guidance.
- Patients require recommendations balancing accuracy, service quality, and diverse expertise.
- Data sparsity in patient-physician interactions complicates effective recommendation systems.
Purpose of the Study:
- To develop a multiobjective physician recommendation method optimizing accuracy, service quality, and expertise diversity.
- To address the challenge of data sparsity in online healthcare recommendation systems.
Main Methods:
- Proposed dynamic graph-based bacteria colony optimization for multiobjective physician recommendation (DyGMO-PR).
- Integrated bacterial colony optimization with an evolving physician relationship graph.
- Utilized a novel graph-based encoding, chemotaxis-inspired graph-walking, and dynamic graph evolution for sparse data.
Main Results:
- DyGMO-PR outperformed 6 state-of-the-art algorithms on a real-world dataset.
- Achieved high accuracy (0.876), service quality (0.873), and diversity (0.720) at K=6.
- Demonstrated superior hypervolume (0.696) and recall (0.380) values, with interpretable results.
Conclusions:
- DyGMO-PR provides an effective solution for multiobjective physician recommendation.
- Offers a flexible foundation for developing responsive and reliable online healthcare recommender systems.
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