Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions. Existing approaches focus on predicting all medications for the current visit, which often overlaps with medications from previous visits. A more clinically relevant task is to identify medication changes. In this paper, we propose a new recurrent residual network, named MICRON, for medication change prediction. MICRON takes the changes in patient health records as input and learns to update a hidden medication vector and the medication set recurrently with a reconstruction design. The medication vector is like the memory cell that encodes longitudinal information of medications. Unlike traditional methods that require the entire patient history for prediction, MICRON has a residual-based inference that allows for sequential updating based only on new patient features (e.g., new diagnoses in the recent visit) more efficiently. We evaluated MICRON on real inpatient and outpatient datasets. MICRON achieves 3.5% and 7.8% relative improvements over the best baseline in F1 score, respectively. MICRON also requires fewer parameters, which significantly reduces the training time to 38.3s per epoch with 1.5x speed-up.

中文翻译：

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变化的重要性：使用递归残差网络的药物变化预测

深度学习正在彻底改变预测性医疗保健，包括向具有复杂健康状况的患者推荐药物治疗。现有方法侧重于预测当前访问的所有药物，这通常与以前访问的药物重叠。临床上更相关的任务是识别药物变化。在本文中，我们提出了一种新的递归残差网络，称为MICRON，用于药物变化预测。MICRON将患者健康记录中的更改作为输入，并学会通过重建设计来反复更新隐藏的药物载体和药物组。药物向量就像是存储单元，可对药物的纵向信息进行编码。与传统的方法需要整个患者的病历进行预测的方法不同，MICRON具有基于残差的推论，该推论允许仅根据新的患者特征（例如，最近就诊时的新诊断）进行有效的顺序更新。我们根据实际的住院和门诊数据集对MICRON进行了评估。MICRON相对于F1分数的最佳基准分别实现了3.5％和7.8％的相对改进。MICRON还需要更少的参数，从而以1.5倍的速度将训练时间显着减少至每个时期38.3s。