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Data Processing and Text Mining Technologies on Electronic Medical Records: A Review.

Wencheng Sun1, Zhiping Cai1, Yangyang Li2

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Electronic Medical Records (EMR) require preprocessing for effective data mining due to quality issues. This study details EMR data processing techniques and text mining applications.

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Area of Science:

  • Health Informatics
  • Data Science
  • Medical Data Analysis

Background:

  • Electronic Medical Records (EMR) are vital for healthcare but present data quality challenges like diversity and incompleteness.
  • Direct data mining of EMR is difficult, necessitating robust preprocessing.
  • Medical text data requires specialized information extraction techniques.

Purpose of the Study:

  • To analyze key techniques for preprocessing Electronic Medical Records (EMR).
  • To explore applications of text mining in EMR analysis.
  • To identify open challenges and future research directions in medical data processing.

Main Methods:

  • Discusses classic preprocessing for structured EMR data: cleansing, integration, transformation, and reduction.
  • Highlights complex methods for semistructured and unstructured medical text, including Named-Entity Recognition (NER) and Relation Extraction (RE).
  • Reviews applications derived from text mining on EMR data.

Main Results:

  • Identifies critical preprocessing steps for improving EMR data quality.
  • Demonstrates the importance of NER and RE for extracting information from medical texts.
  • Outlines current applications and future research avenues in EMR text mining.

Conclusions:

  • Effective EMR data preprocessing is essential for successful data mining and analysis.
  • Advanced text mining techniques are crucial for unlocking insights from complex medical text data.
  • Further research is needed to address challenges in EMR data processing and application development.