no code implementations • 14 Jan 2019 • Ziyuan Pan, Hao Du, Kee Yuan Ngiam, Fei Wang, Ping Shum, Mengling Feng
Compared with the existing models, our method has a number of distinct features: we utilized the accumulative data of patients in ICU; we developed a self-correcting mechanism that feeds errors from the previous predictions back into the network; we also proposed a regularization method that takes into account not only the model's prediction error on the label but also its estimation errors on the input data.
