Data-driven intelligent decision for multimedia medical management

Abstract

Since medical diagnosis runs through the whole journey of human health, the traditional medical diagnosis methods cannot ensure the diagnosis accurate due to the interference of multiple external factors. In response, this paper proposes a multimedia medical management method supported by the data-driven intelligent decision (MMD-DI). This method can be used to predict the survival time of cancer patients under the help of gradient boosting decision tree (GBDT) and hybrid neural network model. First, the feature factors were scanned to match the conditions by GBDT, according to the set value domain; Then the factors were inputted into the neural network. The hybrid neural network was employed to predict the survival time of cancer patients, and it was constructed by combining the convolutional neural network (CNN) and the long short-term memory (LSTM) model. Finally, the stability of the proposed MMD-DI was analyzed, and performance was compared with a series of commonly exploited baseline methods: the mean of cross-validated RMSE (Root Mean Squared Error) evaluation results is 0.183, the mean of cross-validated MAE (Mean Absolute Error) evaluation results is 0.147, the two indicators are both much lower than the commonly exploited baseline methods. A series of experiments proved that MMD-DI has excellent performance and can be used in the multimedia medical management systems.