Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare-猫眼课题宝

DOI：

10.24963/ijcai.2021/486

发表时间：

2021-05

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Chang Lu;Chandan K. Reddy;Prithwish Chakraborty;Samantha Kleinberg;Yue Ning

准确和可解释的健康事件预测对于医疗保健提供者为患者制定护理计划至关重要。电子健康记录（EHR）的可用性使机器学习在提供这些预测方面取得了进展。然而，许多基于深度学习的方法在解决几个关键挑战方面并不令人满意：1）有效利用疾病领域知识; 2）协作学习患者和疾病的表示; 3）合并非结构化特征。为了解决这些问题，我们提出了一个协作图学习模型，以探索病人-疾病的相互作用和医疗领域的知识。我们的解决方案能够捕获患者和疾病的结构特征。该模型还利用非结构化的文本数据，通过采用注意操纵策略，然后将注意文本特征集成到一个顺序的学习过程。我们对两个重要的医疗保健问题进行了广泛的实验，以显示所提出的方法与各种最先进的模型相比具有竞争力的预测性能。我们还通过一组消融和案例研究证实了学习表示和模型可解释性的有效性。

Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (EHR) has enabled machine learning advances in providing these predictions. However, many deep-learning-based methods are not satisfactory in solving several key challenges: 1) effectively utilizing disease domain knowledge; 2) collaboratively learning representations of patients and diseases; and 3) incorporating unstructured features. To address these issues, we propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. Our solution is able to capture structural features of both patients and diseases. The proposed model also utilizes unstructured text data by employing an attention manipulating strategy and then integrates attentive text features into a sequential learning process. We conduct extensive experiments on two important healthcare problems to show the competitive prediction performance of the proposed method compared with various state-of-the-art models. We also confirm the effectiveness of learned representations and model interpretability by a set of ablation and case studies.

Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare

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