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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 semisupervised 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. INDEX TERMS massively parallel computing; deep learning; familial polyposis colorectal cancer; predictive application.

Background Over the past several years, cannabis has become legal for recreational use in many US states and jurisdictions around the world. The opening of these markets has led to the establishment of hundreds of cannabis production and retail firms with accompanying demand for labor, leading to concerns about spillover effects on wages from incumbents. Methods We study the markets for agricultural and retail labor in Washington and Colorado from 2000 to 2019 using differences-in-differences with synthetic controls. We employ employment data from the Quarterly Census of Employment and Wages, state-level demographic data from the US Census Bureau, and agricultural data from the National Agricultural Statistics Service. We use the least absolute shrinkage and selection operator (LASSO) for variable selection and classification and regression trees (CART) for chained imputation of missing values. Results We find little-to-no evidence of a significant difference in weekly wages per worker generated by cannabis legalization: the log of the weekly wage per worker decreases by 0.013 in Washington’s agricultural sector (p value 0.091) and increases by 0.059 in Washington’s retail sector (p value 0.606). Results in Colorado are qualitatively similar. These results are limited in part by the short post-legalization period of the data. Conclusions Cannabis legalization is unlikely to negatively impact incumbent agriculture or retail firms through the labor market channel.

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