Electronic Health Record classification and analysis using NLP Techniques

Electronic Health Record classification and analysis using NLP Techniques

This paper presents an automated system for the classification and analysis of Electronic Health Records (EHRs) using Natural Language Processing (NLP) techniques. The proposed solution integrates text extraction from PDFs and NLP methods to identify and classify EHR content effectively. By leveragi...

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Bibliographic Details
Main Authors: Himavamshi K., Tejaswini D., Sethi Gaurav, Anusuya Devi V.S, Pavani P., Hariharan Shanmugasundaram
Format: Article
Language:English
Published: EDP Sciences 2025-01-01
Series:E3S Web of Conferences
Subjects:
ehr
nlp
pymupdf
stopword
healthcare
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/19/e3sconf_icsget2025_03016.pdf
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Summary:This paper presents an automated system for the classification and analysis of Electronic Health Records (EHRs) using Natural Language Processing (NLP) techniques. The proposed solution integrates text extraction from PDFs and NLP methods to identify and classify EHR content effectively. By leveraging Python libraries such as PyMuPDF for text extraction and applying NLP preprocessing techniques, the system can handle both structured and unstructured data, providing enhanced accuracy in EHR identification. The approach is validated using a set of EHR and non-EHR documents, achieving promising results in classification accuracy.
ISSN:2267-1242

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