Application of Massive Parallel Deep Learning Algorithm in the Prediction of Colorectal Carcinogenesis of Familial Polyposis

Based on the massively parallel deep learning algorithm, this paper studies familial polyposis colorectal carcinogenesis, and proposes a semi-supervised multi-task survival analysis method based on deep learning, which transforms the survival analysis problem into multi-timepoint survival probability prediction. The multi-task learning model is composed of semi-supervised learning problems. We use semi-supervised loss and sorting loss to deal with data of censorship and the non-increasing probability of survival probability. It established a prognostic risk prediction model for familial polyposis colorectal cancer based on a semi-supervised logistic regression method and learns from supervised learning from five aspects of discriminating ability, interpretability, and clinical practicality. The method comparison expands the current understanding of the generalization capabilities of different models and provides a reference for the establishment of clinical prediction models. The effectiveness of this method was verified by external data and provided technical support for constructing a prognostic model with application value for multi-center real clinical data. This model has demonstrated better prediction performance than common models in the prognostic task of familial polyposis colorectal cancer, and successfully described the mechanism of action of predictors.