MiranDa：模拟人类医生的学习过程以实现药物推荐的因果推断
MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication Recommendation
AI总结：
本文提出药物推荐模型MiranDa，通过模拟医生学习过程并结合估计住院时长作为反事实结果，在监督学习与强化学习两阶段训练中优化药物组合，在MIMIC-III和MIMIC-IV数据集上显著降低住院时长并提升推荐效果。
摘要：
为了从药理学角度提升治疗效果，我们提出了专为药物推荐设计的MiranDa，这是第一个可操作的模型，能够提供估计住院时长（ELOS）作为反事实结果，用于指导临床实践和模型训练。具体而言，MiranDa通过两个由ELOS驱动的梯度缩放阶段来模拟医生的教育轨迹：基于证据的训练阶段利用监督学习，而治疗优化阶段则以梯度空间中的强化学习为基础，通过来自ELOS的扰动探索最优药物。在Medical Information Mart for Intensive Care III数据集和IV数据集上的评估显示，我们的模型在五项指标上取得了优异结果，尤其是在降低ELOS方面。令人惊讶的是，我们的模型提供了在双曲空间中证明的药物组合结构属性，并倡导“特定手术操作”的药物组合。这些发现表明MiranDa提升了药物疗效。值得注意的是，我们的范式几乎可以应用于所有医疗任务以及那些具有可用于评估预测结果信息的任务。MiranDa模型的源代码可在https://github.com/azusakou/MiranDa获取。
英文摘要：
To enhance therapeutic outcomes from a pharmacological perspective, we propose MiranDa, designed for medication recommendation, which is the first actionable model capable of providing the estimated length of stay in hospitals (ELOS) as counterfactual outcomes that guide clinical practice and model training. In detail, MiranDa emulates the educational trajectory of doctors through two gradient-scaling phases shifted by ELOS: an Evidence-based Training Phase that utilizes supervised learning and a Therapeutic Optimization Phase grounds in reinforcement learning within the gradient space, explores optimal medications by perturbations from ELOS. Evaluation of the Medical Information Mart for Intensive Care III dataset and IV dataset, showcased the superior results of our model across five metrics, particularly in reducing the ELOS. Surprisingly, our model provides structural attributes of medication combinations proved in hyperbolic space and advocated "procedure-specific" medication combinations. These findings posit that MiranDa enhanced medication efficacy. Notably, our paradigm can be applied to nearly all medical tasks and those with information to evaluate predicted outcomes. The source code of the MiranDa model is available at https://github.com/azusakou/MiranDa.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
