State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Artif Intell Med. 2023 Sep;143:102613. doi: 10.1016/j.artmed.2023.102613. Epub 2023 Jun 12.
The medication recommendation (MR) or medication combination prediction task aims to predict effective prescriptions given accurate patient representations derived from electronic health records (EHRs), which contributes to improving the quality of clinical decision-making, especially for patients with multi-morbidity. Although in recent years deep learning technology has achieved great success in MR, the performance of current multi-label based MR solutions is unsatisfactory. They mainly focus on improving the patient representation module and modeling the medication label dependencies such as drug-drug interaction (DDI) correlation and co-occurrence relationship. However, the hierarchical dependency among medication labels and diversity of difficulty among MR training examples lack sufficient consideration. In this paper, we propose a framework of Curriculum learning Enhanced Hierarchical multi-label classification for MR (CEHMR). Motivated by the category hierarchy of medications which organizes standard medication codes in a hierarchical structure, we utilize it to provide more trustworthy prior knowledge for modeling label dependency. Specifically, we design a hierarchical multi-label classifier with a learnable gate fusion layer, to simultaneously capture the level-independent (local) and level-dependent (global) hierarchical information in the medication hierarchy. In addition, to overcome the diversity of training example difficulties, and progressively achieve a smoother training process, we introduce a bootstrap-based curriculum learning strategy. Hence, the example difficulty can be measured based on the predictive performance of the MR model, and then all training examples would be retrained from easy to hard under the guidance of a predefined training scheduler. Experiments on the real-world medical MIMIC-III database demonstrate that the proposed framework can achieve state-of-the-art performance compared with seven representative baselines, and extensive ablation studies validate the effectiveness of each component of CEHMR.
药物推荐（MR）或药物组合预测任务旨在预测有效的处方，前提是从电子健康记录（EHR）中获得准确的患者表示，这有助于提高临床决策的质量，特别是对于患有多种疾病的患者。尽管近年来深度学习技术在 MR 方面取得了巨大成功，但当前基于多标签的 MR 解决方案的性能并不令人满意。它们主要侧重于改进患者表示模块，并对药物标签依赖关系进行建模，例如药物-药物相互作用（DDI）相关性和共同出现关系。然而，药物标签之间的层次依赖关系和 MR 训练示例的难度多样性缺乏足够的考虑。在本文中，我们提出了一种用于 MR 的课程学习增强层次多标签分类框架（CEHMR）。受药物分类层次结构的启发，该结构以层次结构组织标准药物代码，我们利用它为建模标签依赖关系提供更可靠的先验知识。具体来说，我们设计了一个具有可学习门控融合层的层次多标签分类器，以同时捕获药物层次结构中的水平独立（局部）和水平依赖（全局）层次信息。此外，为了克服训练示例难度多样性并逐步实现更平滑的训练过程，我们引入了基于引导的课程学习策略。因此，可以根据 MR 模型的预测性能来衡量示例难度，然后在预定义的训练调度程序的指导下，从易到难重新训练所有训练示例。在真实的医疗 MIMIC-III 数据库上的实验表明，与七个代表性基线相比，所提出的框架可以实现最先进的性能，并且广泛的消融研究验证了 CEHMR 每个组件的有效性。
