Intelligent data processing method for clinical research

By constructing a clinical knowledge graph and using deep learning models for entity recognition and data quality verification, the difficulties in integrating multi-source heterogeneous clinical data and the problem of semantic ambiguity were solved, achieving efficient and accurate data fusion and processing, and improving the automation level and reliability of clinical research results.

CN121506527APending Publication Date: 2026-02-10YESU (SUZHOU) INTELLIGENT TECH CO LTD
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Patent Information

Application Number
CN202511663117.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-13
Publication Date
2026-02-10

AI Technical Summary

Technical Problem

Existing technologies face challenges in processing multi-source heterogeneous clinical data, including difficulties in data integration, semantic ambiguity, low quality, and poor interoperability. In particular, the lack of a unified semantic framework in large-scale, multi-center studies leads to reliance on manual intervention for data cleaning, which can easily introduce subjective biases and affect the scientific validity and reproducibility of research results.

Method used

A clinical ontology knowledge graph is constructed, and a deep learning model is used for entity recognition and relation extraction. This enables semantic mapping and fusion of data to knowledge graph nodes. Automated data quality verification and repair are performed through graph constraints, ultimately generating a structured and standardized unified dataset.

Benefits of technology

It enables seamless integration of cross-source data, improves the automation level of data processing and the depth of information utilization, ensures the accuracy and reliability of datasets, and improves the efficiency of data preparation and the reproducibility of research results in clinical research.

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Abstract

The invention relates to the technical field of electric digital data processing, and discloses an intelligent data processing method for clinical research. The method comprises the following steps: constructing a basic clinical knowledge graph fused with an international medical ontology; the method comprises the following steps: performing deep learning driven entity recognition and knowledge graph linking on original data from heterogeneous sources such as an electronic medical record and an inspection system; executing cross-source entity alignment and knowledge fusion based on the graph attention network; performing automatic data quality verification and restoration according to clinical logic rules embedded in the atlas; and finally, extracting and generating a standardized analysis ready data set from the enhanced atlas according to research requirements. According to the technical scheme, high-quality, automatic and semantic integration and flexible delivery of clinical research data are realized, and the data processing efficiency and research reliability are improved.
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