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

Abstract

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.

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