动态调整新旧药物关注，提升复杂病患用药推荐精度
ARMR: Adaptively Responsive Network for Medication Recommendation
- 分段时间学习+自适应响应机制，区分新旧用药历史
- 在MIMIC-III/IV上超越现有模型，推荐更精准
- 适合需要个性化用药决策的医疗AI研究者
药物推荐在复杂病情患者治疗中至关重要。现有方法难以平衡历史用药与新药引入。为此，我们提出自适应响应网络（ARMR），包含：1）分段时间学习组件，区分近期与远期病史，实现更细致的时间理解；2）自适应响应机制，根据患者当前状态和用药史动态调整对新旧药物的关注度。在MIMIC-III和MIMIC-IV数据集上的实验表明，ARMR在多种评估指标上优于当前最优基线，有助于实现更个性化、准确的药物推荐。代码已开源：https://github.com/seucoin/armr2。
原文摘要 · Abstract (English)
Medication recommendation is a crucial task in healthcare, especially for patients with complex medical conditions. However, existing methods often struggle to effectively balance the reuse of historical medications with the introduction of new drugs in response to the changing patient conditions. In order to address this challenge, we propose an Adaptively Responsive network for Medication Recommendation (ARMR), a new method which incorporates 1) a piecewise temporal learning component that distinguishes between recent and distant patient history, enabling more nuanced temporal understanding, and 2) an adaptively responsive mechanism that dynamically adjusts attention to new and existing drugs based on the patient's current health state and medication history. Experiments on the MIMIC-III and MIMIC-IV datasets indicate that ARMR has better performance compared with the state-of-the-art baselines in different evaluation metrics, which contributes to more personalized and accurate medication recommendations. The source code is publicly avaiable at: https://github.com/seucoin/armr2.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。