发表机构
Gwangju Institute of Science and Technology (GIST)（光州科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对住院期间用药变更预测，提出R-GEAN非对称候选评分网络，在24万次入院数据上以编辑综合指标0.464优于基线，揭示完整方案与编辑级评估的差异。
AI 中文摘要
患者在住院期间，其处方药物常常会发生变化，因为临床医生会开始、停止或继续某些治疗。我们研究模型能否预测从入院后24小时到出院之间，哪些药物类别被添加或移除。比较完整出院方案的指标可能会奖励那些复制未变化药物的模型，即使它们没有识别出任何实际变化。因此，我们引入了一个防泄漏的基准，仅使用既往完成的入院记录和当前入院前24小时内可获得的信息，来预测净ATC3级别的添加和移除。添加候选者是24小时时未激活的类别，而移除候选者是当时激活的类别。我们还引入了R-GEAN，一个具有独立添加和移除预测器的非对称候选评分网络。在来自82,286名患者的240,480次入院中，R-GEAN在添加、移除、方案变更和动作模式的预定义综合指标（称为编辑综合指标）上取得了最高得分（0.464），而最强的主要比较器为0.435。重新实现的RETAIN、GAMENet和MICRON基线分别获得0.428、0.420和0.288。R-GEAN的优势集中在正确识别出院时不再活跃的药物类别，而罕见添加和多次用药变更的入院仍然困难。基于重建出院方案的micro-F1排名与编辑综合指标在评估模型之间相关性较弱（Spearman r = 0.20）。延续基线尽管未预测任何添加或移除，却取得了最高的完整方案得分。这些结果表明，完整方案评估和编辑级别评估衡量了药物预测的不同方面。该基准评估的是观察到的处方变更，而非治疗适当性。
英文摘要
The medications prescribed to a patient often change during a hospital admission as clinicians start, stop, or continue therapies. We study whether models can predict which medication classes are added or removed between 24 hours after admission and discharge. Metrics that compare the complete discharge regimen can reward models for copying medications that remain unchanged, even when they identify no actual changes. We therefore introduce a leakage-controlled benchmark that predicts net ATC3 additions and removals using only prior completed admissions and information available within the first 24 hours of the current admission. Addition candidates are classes not active at 24 hours, whereas removal candidates are classes active at that time. We also introduce R-GEAN, an asymmetric candidate-scoring network with independent addition and removal predictors. Across 240,480 admissions from 82,286 patients, R-GEAN achieves the highest predefined summary of addition, removal, changed-regimen, and action-pattern performance, termed the edit composite (0.464), compared with 0.435 for the strongest primary comparator. Reimplemented RETAIN, GAMENet, and MICRON baselines obtain 0.428, 0.420, and 0.288, respectively. R-GEAN's advantage is concentrated in correctly identifying medication classes no longer active at discharge, while rare additions and admissions with multiple medication changes remain difficult. Rankings based on micro-F1 over the reconstructed discharge regimen and the edit composite correlate weakly across the evaluated models (Spearman r = 0.20). The continuation baseline achieves the highest complete-regimen score despite predicting no additions or removals. These results show that complete-regimen and edit-level evaluation measure different aspects of medication prediction. The benchmark evaluates observed prescribing changes, not treatment appropriateness
