Research And Development Of Data-driven Colorectal Cancer Prognosis System

Colorectal cancer(CRC)is one of the cancers with high morbidity and mortality.Clinical prognosis plays an important role in the diagnosis and treatment of colorectal cancer.Physicians can predict specific prognostic events that may occur next in the patient based on specific clinical guidelines or predictive models.The current prognosis is mainly based on the expert's clinical experience and consensus,and lacks evidence-based support.With the large-scale application of electronic health records in recent years and the continuous development of machine learning algorithms in the computer field,predictive models based on large-scale clinical data are playing an increasingly important role in prognosis prediction.This thesis is based on the classical algorithm logistic regression model in machine learning,inspired from the multi-factor meta-regression analysis,which is widely used in the medical field,and the model aggregation framework based on Bayesian prior probability theory,which is suitable for a variety of situations.In a variety of scenarios of colorectal cancer prognosis models,and using a combination of public data sets and real data sets,two sets of experiments were designed to validate these models from different perspectives.On this basis,with the above model as the core,a variety of network application development techniques as tools,this thesis designed and implemented a colorectal cancer prognosis prediction system.The system can be customized by the researcher or doctor team to set up research plans and model training programs according to the source of the training data to meet the needs of users' research and prognosis prediction.The server side of the system integrates the data storage module and the data analysis module,and the client and the server exchange data in the manner of network application service,which can conveniently support the teams in different regions.