Yuwen Chen1, 2, 3, *, Xiaolin Qin1, 3, Lige Zhang1, 3, Bin Yi4
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 495-510, 2020, DOI:10.32604/cmc.2020.011278
Abstract The occurrence of perioperative heart failure will affect the quality of medical
services and threaten the safety of patients. Existing methods depend on the judgment of
doctors, the results are affected by many factors such as doctors’ knowledge and
experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a
mixture prediction model is proposed for perioperative adverse events of heart failure,
which combined with the advantages of the Deep Pyramid Convolutional Neural
Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was
used to automatically extract features from patient’s diagnostic texts, and the text… More >
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