计算机科学
机器学习
图形
人工智能
特征工程
疾病
编码器
医学分类
医疗保健
知识图
嵌入
数据科学
数据挖掘
深度学习
理论计算机科学
医学
经济增长
操作系统
护理部
病理
经济
作者
Muchao Ye,Suhan Cui,Yaqing Wang,Junyu Luo,Cao Xiao,Fenglong Ma
标识
DOI:10.1145/3442381.3449860
摘要
The broad adoption of electronic health records (EHR) data and the availability of biomedical knowledge graphs (KGs) on the web have provided clinicians and researchers unprecedented resources and opportunities for conducting health risk predictions to improve healthcare quality and medical resource allocation. Existing methods have focused on improving the EHR feature representations using attention mechanisms, time-aware models, or external knowledge. However, they ignore the importance of using personalized information to make predictions. Besides, the reliability of their prediction interpretations needs to be improved since their interpretable attention scores are not explicitly reasoned from disease progression paths. In this paper, we propose MedPath to solve these challenges and augment existing risk prediction models with the ability to use personalized information and provide reliable interpretations inferring from disease progression paths. Firstly, MedPath extracts personalized knowledge graphs (PKGs) containing all possible disease progression paths from observed symptoms to target diseases from a large-scale online medical knowledge graph. Next, to augment existing EHR encoders for achieving better predictions, MedPath learns a PKG embedding by conducting multi-hop message passing from symptom nodes to target disease nodes through a graph neural network encoder. Since MedPath reasons disease progression by paths existing in PKGs, it can provide explicit explanations for the prediction by pointing out how observed symptoms can finally lead to target diseases. Experimental results on three real-world medical datasets show that MedPath is effective in improving the performance of eight state-of-the-art methods with higher F1 scores and AUCs. Our case study also demonstrates that MedPath can greatly improve the explicitness of the risk prediction interpretation.1
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