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Extraction from Medical Records.

Aleksei Dudchenko1, Polina Dudchenko1, Matthias Ganzinger2

  • 1National Research Tomsk Polytechnic University, Tomsk, Russia.

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|June 4, 2019
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Summary
This summary is machine-generated.

This study introduces a machine learning system for extracting data from Russian medical texts. The best model achieved a high F-score, improving data accessibility for analysis and decision support.

Keywords:
NLPdata extractionmachine learningmedical records

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Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Machine Learning

Background:

  • Free narrative text remains prevalent in electronic medical records, hindering statistical analysis and decision support.
  • Current limitations prevent the effective utilization of unstructured medical text data.
  • Automated data extraction is crucial for leveraging the information within clinical narratives.

Purpose of the Study:

  • To develop and evaluate a medical data extraction system for Russian language free-text records.
  • To compare the performance of different artificial neural network architectures for this task.
  • To enhance the usability of unstructured medical data for computational analysis.

Main Methods:

  • Development of a prototype medical data extraction system using supervised machine learning.
  • Implementation and comparison of various artificial neural network architectures (CNN, MLP).
  • Utilizing large pre-trained word2vec models for text embedding.

Main Results:

  • The system demonstrated high performance in extracting data from Russian medical texts.
  • A combination of a Convolutional Neural Network (CNN) prediction model and a large pre-trained word2vec model achieved the best F-score of 0.9763.
  • A Multilayer Perceptron (MLP) model with the same word embedding yielded a comparable F-score of 0.9741.

Conclusions:

  • Supervised machine learning, particularly neural networks, can effectively process and extract data from unstructured Russian medical texts.
  • The developed system significantly improves the accessibility of medical record data for statistical analysis and decision support.
  • The findings highlight the potential of advanced NLP techniques in medical informatics.