CAMP : Co-Attention Memory Networks for Diagnosis Prediction in Healthcare

Gao, Jingyue and Wang, Xiting and Wang, Yasha and Yang, Zhao and Gao, Junyi and Wang, Jiangtao and Tang, Wen and Xie, Xing (2020) CAMP : Co-Attention Memory Networks for Diagnosis Prediction in Healthcare. In: 2019 IEEE International Conference on Data Mining (ICDM) :. IEEE. ISBN 9781728146058

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Abstract

Diagnosis prediction, which aims to predict future health information of patients from historical electronic health records (EHRs), is a core research task in personalized healthcare. Although some RNN-based methods have been proposed to model sequential EHR data, these methods have two major issues. First, they cannot capture fine-grained progression patterns of patient health conditions. Second, they do not consider the mutual effect between important context (e.g., patient demographics) and historical diagnosis. To tackle these challenges, we propose a model called Co-Attention Memory networks for diagnosis Prediction (CAMP), which tightly integrates historical records, fine-grained patient conditions, and demographics with a three-way interaction architecture built on co-attention. Our model augments RNNs with a memory network to enrich the representation capacity. The memory network enables analysis of fine-grained patient conditions by explicitly incorporating a taxonomy of diseases into an array of memory slots. We instantiate the READ/WRITE operations of the memory network so that the memory cooperates effectively with the patient demographics through co-attention mechanism. Experiments on real-world datasets demonstrate that CAMP consistently performs better than state-of-the-art methods.