The present invention discloses a clinical
data analysis method and
system for
hematological tumor patients based on knowledge graphs, which relates to the field of
medical information processing technology. The present invention utilizes a
natural language processing model to parse unstructured text and construct a standardized entity set. Laboratory indicators are aggregated through dynamic windows to construct a timeline
data set annotated with treatment stages. Standardized entities are integrated based on a
hematological tumor ontology model, and a dynamic global
knowledge graph is constructed in combination with clinical causal relationships and temporal paths to achieve
chain reaction warnings. Based on real-time patient subgraphs, successful cases with the same molecular characteristics are retrieved in the
knowledge graph to generate a set of candidate treatment plans, and a graph neural network trained with an optimized
algorithm is used to predict the risk of complications of each plan, and finally the
optimal treatment plan is evaluated and selected. The present invention breaks through the barriers of clinical data, realizes dynamic reasoning, advanced risk warning and personalized plan optimization throughout the treatment cycle, and significantly improves the accuracy, safety and efficiency of medical
resource utilization in diagnosis and treatment.