The invention relates to the technical field of
medical diagnosis and
artificial intelligence, in particular to a urinary tract
disease prediction system based on multi-
modal chromosome abnormality and clinical data, which integrates
demographic statistics, clinical symptoms, laboratory detection,
molecular biology of nine key
chromosome sites and multi-dimensional data of images, and performs pre-
processing and single-
modal feature extraction to obtain a prediction result of the urinary tract
disease. Fusion features are generated in a targeted mode through a task self-adaption fusion module, and then urinary tract
epithelial cancer or
neoplastic lesion positive prediction,
positive sample TNM staging and
pathological grading prediction and focus origin positioning are achieved through a multi-task model. According to the
system, an edge cloud collaborative architecture is adopted, the computing power requirements of different medical institutions are met, the feature contribution degree is determined through an SHAP method, a visual
clinical report is generated, the problems that in the prior art, multi-
modal data integration is insufficient, and non-invasive staging and grading are lacked are solved, prediction reliability and clinical adaptability are improved, and support is provided for clinical auxiliary
decision making.