Provided is a multi-center synergetic cancer prognosis prediction system based on multi-source migration learning. The system comprises a model parameter setting module, a data screening module and a multi-source migration learning module, wherein the model parameter setting module is used for setting cancer prognosis prediction model parameters; the data screening module is arranged at a clinical center, and a management center transmits the set model parameters to each clinical center, such that each clinical center queries sample features and prognosis index data from a local database according to the model parameters, so as to preprocess the data; and the multi-source migration learning module comprises a source model training unit, a migration weight calculation unit, and a target model calculation unit. The use of multi-source migration learning to solve the problem of heterogeneous data between a source center and a target center and the problem of insufficient label data at the target center makes it possible to construct a more accurate prediction model taking the heterogeneous data at a plurality of centers into consideration. Meanwhile, complementation and sharing of original data from all institutions during a model training process are realized, thereby avoiding breaching the privacy of patients.
展开▼
