H3通过三跳路径建模，提升医生转诊网络预测准确率。
H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction
- 基于中间医生的三跳路径建模，结合度归一化与去冗余惩罚。
- 在跨周期与同期预测中均优于传统方法与深度学习基线。
- 结果可追溯至具体中间医生，适合医疗协同场景部署。
精准预测医生转诊关系对优化医疗协作、减少服务碎片化至关重要。然而，现有计算方法（从三元闭包启发式到图神经网络）未能捕捉医生转诊网络的固有特性，如稀疏性、异配度混合及枢纽主导拓扑。本文提出H3——一种面向医疗领域的三跳索引，通过建模经由中间医生的间接转诊路径，结合度归一化与冗余惩罚机制，有效缓解枢纽驱动的噪声。基于Medicare医生共用患者数据，我们在两种互补预测范式下评估：同期预测（在稀疏条件下恢复同时期转诊链接）与跨周期预测（测试随转诊窗口扩展的时间转移鲁棒性）。在两种场景下，H3持续优于经典启发式与基于深度学习的基线模型。与黑箱神经网络不同，H3生成的预测可完全分解，追踪至特定中间医生，为转诊网络补全提供透明且可部署的解决方案。
原文摘要 · Abstract (English)
Accurate prediction of physician referral links is essential for optimizing care coordination and reducing fragmentation in healthcare delivery. However, existing computational methods, ranging from triadic closure heuristics to graph neural networks, fail to capture the intrinsic properties of physician referral networks, including sparsity, disassortative degree mixing, and hub-dominated topology. Here, we propose H3, a healthcare three-hop index that addresses these limitations by modeling indirect referral pathways through intermediate physicians, with degree-based normalization and a redundancy penalty to mitigate hub-mediated noise. Using Medicare Physician Shared Patient Patterns data, we evaluate H3 under two complementary prediction regimes: within-period prediction, which assesses recovery of contemporaneous referral links under sparse conditions, and cross-period prediction, which tests robustness to temporal shift as referral windows expand. Across both regimes, H3 consistently outperforms classical heuristics and deep learning-based baselines. Unlike black-box neural network approaches, H3 produces fully decomposable predictions traceable to specific intermediary physicians, offering a transparent and deployable solution for referral network completion.
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