The invention discloses a multi-source collaborative dynamic
time sequence TCP prediction method and
system, a medium and a program product, and the method comprises the steps: collecting multi-source heterogeneous data, including DVH data, CT data, CPP data and longitudinal follow-up data of a plurality of time nodes, of a patient in a whole radiotherapy period; performing hierarchical preprocessing and
data format unification on the multi-source heterogeneous data; constructing a dynamic
time sequence feature fusion matrix containing a
time dependency relationship; constructing a
deep learning model integrating a
time sequence processing unit and a cross-
modal fusion unit, wherein the model supports an
incremental learning iteration updating mechanism; using the model to output TCP prediction results of a new patient at different time nodes; and if the new patient is a special case, calling the clinical rule
adaptation model, and correcting the TCP prediction result by adopting a mixed correction strategy combining
rule matching and doctor experience weight. According to the method, multi-source heterogeneous data can be deeply fused, time dynamic association is captured,
modal feature contribution degree is quantified, continuous iterative updating is supported, and individual differences are adapted.