The invention belongs to the technical field of
data processing, and particularly discloses a method and
system for predicting the lifetime of a non-
small cell lung cancer patient after radiotherapy, and the method comprises the steps: collecting multi-time-point imaging and
hematology follow-up visit data of the patient after radiotherapy, building a longitudinal follow-up visit sequence, and mapping the longitudinal follow-up visit sequence to a unified time axis; determining a bimodal first-time significant improvement point through preset rule judgment, and constructing and standardizing an asynchronous index according to the bimodal first-time significant improvement point; then inputting a
feature vector at a follow-up time point, encoding a bimodal sequence through a Transform
time sequence encoder, and generating joint representation through cross-
modal interaction; and finally, inputting the standardized asynchronous index, the joint representation and the clinical and radiotherapy characteristics into a DeepHit model, and outputting a
patient survival distribution prediction result. According to the method, bimodal indexes can be accurately captured, time
dislocation is improved, prediction accuracy and individualization degree are improved, and a reliable basis is provided for
clinical prognosis evaluation.