The application provides a
blood stream infection related
septic shock multi-
modal fusion prediction method and
system, belonging to the field of intelligent
medical treatment, and solving the problems of long time consumption, insufficient sensitivity and insufficient multi-
modal information analysis capability in
blood stream infection related
septic shock. The method comprises: acquiring multi-
modal clinical data of a target patient suspected of
blood stream infection within a preset
observation time window to obtain a multi-modal multi-scale feature sequence formed by combining each modality spatiotemporal
feature vector sequence; inputting the multi-modal multi-scale feature sequence into a
time sequence encoding network based on a multi-head attention mechanism to obtain an aligned multi-modal
time sequence representation sequence; setting a learnable modality weight parameter for each modality, and updating the learnable modality weight parameter, the
encoder and the
time sequence encoding network through a task
loss function according to the
label of whether blood
stream infection related
septic shock occurs or not in the training process to obtain a multi-modal fusion model for prediction.