Exploration and Practice of Constructing Structured Specialized Disease Database based on NLP for Medical Records

Corresponding author: ZHANG YANAN, 18964647796@163.com

DOI: 10.12201/bmr.202406.00007

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  Abstract: Objective/Meaning To slove the problems of coarse granularity and poor interoperability of unstructured electronic medical record generated during the diagnosis and treatment of psoriasis, this article provides a construction approach based on artificial intelligence technology to generate structured electronic medical records. A psoriasis specific disease database system is constructed to achieve the goal of precise and use medical record data. Process/Methods Transform unstructured electronic medical records into structured electronic medical records using artificial intelligence technologies such as templated input and natural language processing. Results/Conclusion The innovative practice of psoriasis specific disease database based on NLP has explored feasible solutions for constructing a standardized and standardized specific disease database. The multi center research and disease platform built on a single disease database has further achieved the scale development of disease databases, providing clinical researchers with unified, complete, and efficient data sources, improving the efficiency of clinical research work, and promoting high-quality development of clinical research work.

  Key words: NLP; templated input; natural language processing; structured electronic medical records; specific disease database

  Submit time: 7 June 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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