A Hybrid Residual Network and Long Short-Term Memory Method for Peptic Ulcer Bleeding Mortality Prediction
Publication in refereed journal

摘要The prediction of patient mortality, which can detect high-risk patients, is a significant yet challenging problem in medical informatics. Thanks to the wide adoption of electronic health records (EHRs), many data-driven methods have been proposed to forecast mortality. However, most existing methods do not consider correlations between static and dynamic data, which contain significant information about mutual influences between these data. In this paper, we utilize a deep Residual Network (ResNet) consisting of many convolution units, which can jointly analyze different variables, to capture correlation information in and between static and dynamic variables. Furthermore, the Long Short-Term Memory (LSTM) method is used to extract temporal dependencies information from dynamic data. Finally, a deep fusion method is used to integrate these different types of information to improve mortality prediction. Experiment results on Peptic Ulcer Bleeding (PUB) mortality prediction show that the proposed method outperforms existing methods and achieves an AUC (area under the receiver operating characteristic curve) score of 0.9353.
著者Tan Q, Ma AJ, Deng, Wong VW, Tse YK, Yip TC, Wong GL, Ching JY, Chan FK, Yuen PC
期刊名稱AMIA ... Annual Symposium proceedings. AMIA Symposium
頁次998 - 1007

上次更新時間 2021-19-06 於 00:01