The invention is applicable to the technical field of clinical
medicine, provides a clinical multi-
modal cancer drug response prediction method based on feature reconstruction, constructs a clinical multi-
modal model for
drug response prediction of diffuse large B-
cell lymphoma, and aims to predict the
drug response of diffuse large B-
cell lymphoma by integrating
gene sequencing and clinical multi-
modal data. And accurate
drug reaction prediction is realized. The model adopts an end-to-end multi-stage
processing flow: firstly, extracting
gene features through TransP-Net, and
processing multi-modal clinical data by using a
clinical information encoder; then, pseudo-
gene features are generated through a clinical-
genome filling module to deal with the data missing problem; and finally, multi-modal deep fusion is realized through a
clinical information decoder, and a prediction result is output. According to the method, data characteristics and working processes in a real clinical environment are fully considered, two conditions of complete gene data and missing gene data can be processed at the same time, and the method has a good clinical transformation prospect and application value.