We address the medication recommendation problem, which aims to recommend
effective medications for a patient's current visit by utilizing information
(e.g., diagnoses and procedures) given at the patient's current and past
visits. While there exist a number of recommender systems designed for this
problem, we point out that they are challenged in accurately capturing the
relation (spec., the degree of relevance) between the current and each of the
past visits for the patient when obtaining her current health status, which is
the basis for recommending medications. To address this limitation, we propose
a novel medication recommendation framework, named VITA, based on the following
two novel ideas: (1) relevant-Visit selectIon; (2) Target-aware Attention.
Through extensive experiments using real-world datasets, we demonstrate the
superiority of VITA (spec., up to 5.56% higher accuracy, in terms of Jaccard,
than the best competitor) and the effectiveness of its two core ideas. The code
is available at this https URL

提出了一种名为 VITA 的新型药物推荐框架，通过相关访问的选择和目标感知的注意机制，解决了现有推荐系统在获取患者当前健康状态时准确捕捉其与过去每次访问之间关系的挑战，并在真实世界数据集上进行了广泛实验，证明了 VITA 的卓越性能和其两个核心思想的有效性。

VITA: 医药推荐的精心选择和加权少量更佳

VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication  Recommendation

Deep learning-based health status representation learning and clinical
prediction have raised much research interest in recent years. Existing models
have shown superior performance, but there are still several major issues that
have not been fully taken into consideration. First, the historical variation
pattern of the biomarker in diverse time scales plays a vital role in
indicating the health status, but it has not been explicitly extracted by
existing works. Second, key factors that strongly indicate the health risk are
different among patients. It is still challenging to adaptively make use of the
features for patients in diverse conditions. Third, using prediction models as
the black box will limit the reliability in clinical practice. However, none of
the existing works can provide satisfying interpretability and meanwhile
achieve high prediction performance. In this work, we develop a general health
status representation learning model, named AdaCare. It can capture the long
and short-term variations of biomarkers as clinical features to depict the
health status in multiple time scales. It also models the correlation between
clinical features to enhance the ones which strongly indicate the health status
and thus can maintain a state-of-the-art performance in terms of prediction
accuracy while providing qualitative interpretability. We conduct a health risk
prediction experiment on two real-world datasets. Experiment results indicate
that AdaCare outperforms state-of-the-art approaches and provides effective
interpretability, which is verifiable by clinical experts.

该项研究提出了一种 deep learning-based 的健康状态表示学习模型 AdaCare，能够在多个时间尺度上捕捉生物标志物的长期和短期变化，强化指示健康状态的特征，并提供有效的可解释性和高预测准确率，实验证明 AdaCare 胜过同类方法，通过临床专家验证的可解释性也很有效。

通过自适应特征提取和重新校准进行可解释的临床健康状况表示学习

AdaCare: Explainable Clinical Health Status Representation Learning via  Scale-Adaptive Feature Extraction and Recalibration

The Infant Mortality Rate (IMR) is the number of infants per 1000 that do not
survive until their first birthday. It is an important metric providing
information about infant health but it also measures the society's general
health status. Despite the high level of prosperity in the U.S.A., the
country's IMR is higher than that of many other developed countries.
Additionally, the U.S.A. exhibits persistent inequalities in the IMR across
different racial and ethnic groups. In this paper, we study the infant
mortality prediction using features extracted from birth certificates. We are
interested in training classification models to decide whether an infant will
survive or not. We focus on exploring and understanding the importance of
features in subsets of the population; we compare models trained for individual
races to general models. Our evaluation shows that our methodology outperforms
standard classification methods used by epidemiology researchers.

本文旨在通过分析出生证明上所提取的特征，研究婴儿死亡率的预测问题。我们关注于训练分类模型来决定婴儿的存活与否，并比较不同种族之间的差异。我们的评估表明，该方法胜过流行病学研究中所采用的标准分类方法。