Patients with advanced cancer are burdened physically and psychologically, so there is an urgent need to pay more attention to their health-related quality of life (HRQOL). With an expected clinical endpoint prediction, over-treatment can be effectively eliminated by the means of palliative care at the right time. This paper develops a deep learning based approach for cancer clinical endpoint prediction based on patient's electronic health records (EHR). Due to the pervasive existence of categorical information in EHR, it brings unavoidably obstacles to the effective numerical learning algorithms. To address this issue, we propose a novel cross-field categorical attributes embedding (CCAE) model to learn a vectorized representation for cancer patients in attribute-level by orders, in which the strong semantic coupling among categorical variables are well exploited. By transforming the order-dependency modeling into a sequence learning task in an ingenious way, recurrent neural network is adopted to capture the semantic relevance among multi-order representations. Experimental results from the SEER-Medicare EHR dataset have illustrated that the proposed model can achieve competitive prediction performance compared with other baselines.

晚期癌症患者身心负担沉重，因此迫切需要关注他们的健康相关生活质量（HRQOL）。通过预期的临床终点预测，可以通过适时的姑息治疗有效地消除过度治疗。本文开发了一种基于深度学习的基于患者电子健康记录 (EHR) 的癌症临床终点预测方法。由于 EHR 中普遍存在分类信息，不可避免地给有效的数值学习算法带来了障碍。为了解决这个问题，我们提出了一种新的跨领域分类属性嵌入（CCAE）模型，以按顺序在属性级别学习癌症患者的矢量化表示，其中分类变量之间的强语义耦合得到了很好的利用。通过巧妙地将顺序依赖建模转化为序列学习任务，采用循环神经网络来捕获多阶表示之间的语义相关性。SEER-Medicare EHR 数据集的实验结果表明，与其他基线相比，所提出的模型可以实现具有竞争力的预测性能。