2026-10-12T15:25:51Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33810220 2026-09-15T17:29:17Z category_7 category_21 category_734 category_106 portal_5 item_type_3 month_year_09_2026

<p>Comparison of matching quality representations.</p> Yan Qiu (141500) Yi Chen (38576) Yinghong Xie (22319875) Min Li (12799) Junhui Yan (509593) Hu Liu (176964) Medicine Biotechnology Biological Sciences not elsewhere classified Science Policy permuted language modeling online healthcare platforms offers practical implications achieves competitive ratio online physician recommendation unstructured consultation records patient matching quality find online physicians physician service capacity div >< p dynamic capacity constraints capacity constraints construct physician dynamic capacity recommend physicians patient ’ 009 records world dataset validated using study provides study aims statistically significant semantic similarity robustness analysis results show reliable reference hs datasets delayed decision baseline methods based framework approximately 0 <div><p>Existing physician recommendation methods mostly start from the patient’s perspective on online healthcare platforms, neglecting the importance of physician service capacity and semantic similarity. Considering the uncertainty of arrivals and capacity constraints, this study aims to propose a dynamic capacity and semantic similarity-based framework to recommend physicians. We design an online rolling matching algorithm based on delayed decision-making. We employ MPNet, a pre-trained language model based on masked and permuted language modeling, to extract contextual semantic representations from unstructured consultation records and construct physician-patient matching quality. The proposed method is validated using a real-world dataset with 88,009 records from one of the largest online healthcare platforms in China. The results show that our method significantly outperforms baseline methods. It achieves competitive ratio of approximately 0.988 on both CIM and HS datasets, with the largest improvement reaching 0.094 over the baseline methods. The improvements over all baselines are statistically significant. The sensitivity and robustness analysis further demonstrate that the integration of dynamic capacity constraints and MPNet-based semantic similarity enhances the effectiveness and stability of online physician recommendation. This study provides a reliable reference for users to find online physicians and offers practical implications for the design of online healthcare platforms. The implementation of our method is available at <a href="https://github.com/qiuyan-just/dynamic_matching" target="_blank">https://github.com/qiuyan-just/dynamic_matching</a> to facilitate reproducibility.</p></div> 2026-09-15T17:29:08Z Dataset Dataset 10.1371/journal.pone.0357449.t003 https://figshare.com/articles/dataset/_p_Comparison_of_matching_quality_representations_p_/33810220 CC BY 4.0