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Clinical Text Data Categorization and Feature Extraction Using Medical-Fissure Algorithm and Neg-Seq Algorithm.

Naveen S Pagad1,2, Pradeep N3, Khalid K Almuzaini4

  • 1Department of Information Science and Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire 574 240, India.

Computational Intelligence and Neuroscience
|March 17, 2022
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Summary
This summary is machine-generated.

This study introduces advanced machine learning and natural language processing to improve Electronic Health Records (EHRs) data quality. The new methods enhance clinical data extraction accuracy for easier diagnosis and secure cloud storage.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Data Management

Background:

  • Electronic Health Records (EHRs) contain vast patient data but are often unstructured and of low quality.
  • Extracting meaningful clinical information from EHRs presents significant technical challenges.
  • Accurate data extraction is crucial for effective disease identification, treatment, and diagnosis.

Purpose of the Study:

  • To develop and evaluate a novel technique for improving the quality and accuracy of clinical text data extraction from EHRs.
  • To address the challenges of unstructured data and low data quality in EHRs using advanced algorithms.
  • To enhance the security of extracted clinical data through cloud storage with encryption.

Main Methods:

  • Utilized machine learning and natural language processing techniques, including the Medical-Fissure Algorithm (via Halve Progression) and Neg-Seq Algorithm.
  • Employed a cross-validation approach to improve data quality and diagnostic accuracy.
  • Implemented a secure cloud storage solution with a secret key for extracted data.

Main Results:

  • Achieved a high data extraction accuracy of 99.6%.
  • Significantly improved the quality of unstructured clinical text data.
  • Demonstrated efficient data extraction and redundancy removal.
  • Enhanced the ease of clinical diagnosis through improved data quality.

Conclusions:

  • The proposed technique effectively overcomes technical complexities in EHR data extraction.
  • The integration of Medical-Fissure and Neg-Seq algorithms enhances data quality and accuracy.
  • The system provides a secure and efficient method for managing clinical data, supporting better healthcare outcomes.