Global Interpretation for Patient Similarity Learning
Global Interpretation for Patient Similarity Learning
复制标题
DOI：
10.1109/bibm49941.2020.9313255
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Mengdi Huai;Chenglin Miao;Jinduo Liu;Di Wang;Jingyuan Chou;Aidong Zhang
中科院分区：
文献类型：
--
作者：
Mengdi Huai;Chenglin Miao;Jinduo Liu;Di Wang;Jingyuan Chou;Aidong Zhang
作为医疗保健领域的一个重要的学习问题家族，患者相似性学习近年来受到了广泛的关注。患者相似性学习旨在根据一对患者的历史临床信息来测量一对患者之间的相似性，这有助于提高对感兴趣患者的临床预测。尽管患者相似性学习在许多现实世界的应用中取得了巨大的成功，但所学习的患者相似性模型的行为背后缺乏透明度，阻碍了用户信任预测结果，从而阻碍了其在现实世界中的进一步应用。为了解决这个问题，在本文中，我们研究了如何在患者相似性学习中启用解释，并提出了一种用于患者相似性学习的全局解释方法。基于所提出的全局解释方法，我们可以识别数据特征的最小足够子集，这些特征本身足以证明训练有素的患者相似性模型所做的全局预测的合理性。确定的最小充分特征子集可以帮助我们更好地理解不同患者亚群的学习模型的整体行为。我们还对现实世界的数据集进行了实验，以评估所提出的全局解释方法的性能。
As an important family of learning problems in healthcare domain, patient similarity learning has received much attention in recent years. Patient similarity learning aims to measure the similarity between a pair of patients according to their historical clinical information, which helps to improve the clinical predictions of the patient of interest. Although patient similarity learning has achieved tremendous success in many real-world applications, the lack of transparency behind the behavior of the learned patient similarity model impedes users from trusting the predicted results, which hampers its further applications in the real world. To tackle this problem, in this paper, we investigate how to enable interpretation in patient similarity learning and propose a global interpretation method for patient similarity learning. Based on the proposed global interpretation method, we can identify a minimal sufficient subset of data features that are sufficient in themselves to justify the global predictions made by the well-trained patient similarity model. The identified minimal sufficient feature subset can help us to better understand the overall behaviors of the learned model across different subpopulations of patients. We also conduct experiments on real-world datasets to evaluate the performance of the proposed global interpretation method.