The application discloses a multi-
modal diabetes dynamic
treatment effect evaluation method, relates to the technical field of
data processing, and unifies a time axis and marks reliability for multi-
source data through an
adaptive interpolation and alignment strategy; peak values and abnormal fluctuations within one to two weeks are detected and recognized based on a short-term window, a short-term change amount and a risk prompt are generated; a multi-layer attention or a
Transformer is used to fuse data on a monthly scale, comprehensive long-term
treatment effect indexes and key inflection points are extracted; short-term quantitative results and long-term indexes are brought into a multi-target reward mechanism through a continuous learning and interactive real-time feedback module,
model parameters are iterated online, and clinical intervention suggestions are output; external events are quantitatively marked, and identification and response to sudden situations are strengthened through a bias injection mode. High-quality integration of multi-source heterogeneous data, short-term and long-term multi-level analysis are realized, and the precision and timeliness of diabetes
dynamic monitoring and intervention are significantly improved.