A Patient Digital Model Retrieval Method and System Based on Event Graph

By constructing a patient digital model based on event graphs and utilizing a large language model and dynamic weight adjustment mechanism, the problem of insufficient utilization of unstructured data in existing technologies is solved, enabling efficient and accurate patient digital model retrieval and personalized diagnosis and treatment support.

CN122087083APending Publication Date: 2026-05-26CHAO XIAN SHI NENG (BEI JING) KE JI YOU XIAN GONG SI
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-14
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies struggle to fully leverage the value of unstructured or semi-structured medical data when building digital patient models, resulting in inaccurate information matching, an inability to accurately reflect the characteristics of a patient's condition, and a lack of dynamic representation capabilities, making it difficult to support personalized treatment decisions.

Method used

An event graph-based approach is adopted to construct a timeline event graph by intelligently parsing electronic medical records, extract key diagnosis and treatment events using a large language model, and perform patient digital model retrieval by combining a dynamic weight adjustment mechanism, thereby realizing information association across time nodes and multimodal data fusion.

Benefits of technology

It significantly improves the accuracy and clinical relevance of patient digital models, enabling rapid matching of similar cases, supporting the development of personalized treatment plans, improving diagnostic and treatment efficiency and precision, and promoting the development of precision medicine.

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Abstract

This invention provides a method and system for retrieving patient digital models based on event graphs. The method includes: designing a timeline-based event extraction and structuring method for unstructured or semi-structured data in electronic medical records, constructing an event graph of the electronic medical records; extracting key information from the medical record text in the event graph using an intelligent model; designing a patient digital model retrieval algorithm; establishing a key information retrieval and matching mechanism; and quickly searching for digital models similar to the current patient's condition from a historical case database, providing data support for subsequent diagnosis and treatment analysis. This invention can extract key information from massive heterogeneous clinical data, construct patient digital models with high clinical relevance, interpretability, and dynamic adaptability. Based on event graph technology and large-scale model applications, it improves the modeling accuracy and clinical applicability of patient data in the diagnosis and treatment of complex diseases, significantly improving the accuracy, completeness, and clinical relevance of patient digital models.
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