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Patient outcome prediction via convolutional neural networks based on multi-granularity medical concept embedding

机译：基于多粒度医学概念嵌入的卷积神经网络预测患者结果

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摘要

The large availability of biomedical data brings opportunities and challenges to health care. Representation of medical concepts has been well studied in many applications, such as medical informatics, cohort selection, risk prediction, and health care quality measurement. In this paper, we propose an efficient multichannel convolutional neural network (CNN) model based on multi-granularity embeddings of medical concepts named MG-CNN, to examine the effect of individual patient characteristics including demographic factors and medical comorbidities on total hospital costs and length of stay (LOS) by using the Hospital Quality Monitoring System (HQMS) data. The proposed embedding method leverages prior medical hierarchical ontology and improves the quality of embedding for rare medical concepts. The embedded vectors are further visualized by the t-Distributed Stochastic Neighbor Embedding (t-SNE) technique to demonstrate the effectiveness of grouping related medical concepts. Experimental results demonstrate that our MG-CNN model outperforms traditional regression methods based on the one-hot representation of medical concepts, especially in the outcome prediction tasks for patients with low-frequency medical events. In summary, MG-CNN model is capable of mining potential knowledge from the clinical data and will be broadly applicable in medical research and inform clinical decisions.
机译：生物医学数据的巨大可用性给卫生保健带来了机遇和挑战。医学概念的表示已在许多应用中得到了很好的研究，例如医学信息学，队列选择，风险预测和卫生保健质量测量。在本文中，我们提出了一种基于医学概念MG-CNN的多粒度嵌入的高效多通道卷积神经网络（CNN）模型，以检验包括人口统计学因素和医疗合并症在内的各个患者特征对总医院成本和病程的影响使用医院质量监控系统（HQMS）数据的住院时间（LOS）。所提出的嵌入方法利用了先前的医学分层本体，并提高了罕见医学概念的嵌入质量。嵌入的载体通过t分布随机邻居嵌入（t-SNE）技术进一步可视化，以证明对相关医学概念进行分组的有效性。实验结果表明，我们的MG-CNN模型优于基于医学概念一站式表示的传统回归方法，尤其是在低频医学事件患者的结果预测任务中。总之，MG-CNN模型能够从临床数据中挖掘潜在的知识，并将广泛应用于医学研究并为临床决策提供依据。

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