Construction of a Clinical Data Standardization System Based on Medical Data Element and Large Language Model

Abstract

To address challenges such as diverse clinical data sources, inconsistent standards, and difficulties in sharing, this study developed a clinical data standardization system based on medical data element and Large Language Model (LLM). 1. Customizable patient and medical record templates were created using medical data element and related terminology, enabling dynamic binding for data structuring and standardization. 2. LLM was leveraged to perform intelligent information extraction, semantic normalization, and standardization of unstructured or semi-structured clinical text. 3. User permissions were configured to manage medical record data across both record repositories and institutions, supporting cross-repository and cross-institutional export of standardized data. This study offers an alternative solution for integrating multi-source, heterogeneous clinical data while providing high-quality AI-ready data for applications such as data mining and LLM training. This advancement supports research in clinical science, medical artificial intelligence, and related fields.

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