Wonsung Lee, Sungrae Park, Weonyoung Joo, and Il-Chul Moon, Diagnosis Prediction via Medical Context Attention Networks Using Deep Generative Modeling, IEEE International Conference on Data Mining (ICDM 2018), Singapore, Singapore, Nov 17-20, 2018 (Accepted as a short paper)(Acceptance rate : 8.86% for Full paper, 11.08% for Short papers. 948 submissions)

Abstract

Predicting the clinical outcome of patients from the historical electronic health records (EHRs) is a fundamental research area in medical informatics. Although EHRs contain various records associated with each patient, the existing work mainly dealt with the diagnosis codes by employing recurrent neural networks (RNNs) with a simple attention mechanism. This type of sequence modeling often ignores the heterogeneity of EHRs. In other words, it only considers historical diagnoses and does not incorporate patient demographics, which correspond to clinically essential context, into the sequence modeling. To address the issue, we aim at investigating the use of an attention mechanism that is tailored to medical context to predict a future diagnosis. We propose a medical context attention (MCA)-based RNN that is composed of an attention-based RNN and a conditional deep generative model. The novel attention mechanism utilizes the derived individual patient information from conditional variational autoencoders (CVAEs). The CVAE models a conditional distribution of patient embeddings and his/her demographics to provide the measurement of patient's phenotypic difference due to illness. Experimental results showed the effectiveness of the proposed model. 

@INPROCEEDINGS{8594952, 

author={Lee, Wonsung and Park, Sungrae and Joo, Weonyoung and Moon, Il-Chul}, 

booktitle={2018 IEEE International Conference on Data Mining (ICDM)}, 

title={Diagnosis Prediction via Medical Context Attention Networks Using Deep Generative Modeling}, 

year={2018}, 

pages={1104-1109}, 

doi={10.1109/ICDM.2018.00143}

} 

Source Website:

https://ieeexplore.ieee.org/document/8594952