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

Full Text

Times Cited

Other information
AbstractThe 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.
All Author(s) ListTan Q, Ma AJ, Deng, Wong VW, Tse YK, Yip TC, Wong GL, Ching JY, Chan FK, Yuen PC
Journal nameAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume Number2018
Pages998 - 1007
LanguagesEnglish-United Kingdom

Last updated on 2022-08-01 at 23:45