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Natural Language Processing for Substance Use Disorder Information Extraction: A Systematic Literature Review.

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This summary is machine-generated.

This review examined natural language processing (NLP) for substance use disorder (SUD) information extraction. Rule-based NLP methods and concept extraction were most common, with Python as a leading software, achieving good performance metrics.

Keywords:
AddictionInformation extractionNatural language processingSubstance use disorderSystematic review

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Area of Science:

  • Medical Informatics
  • Computational Linguistics
  • Public Health

Background:

  • Substance Use Disorders (SUDs) pose a significant public health challenge.
  • Accurate information extraction from clinical text is crucial for research and decision-making.
  • Natural Language Processing (NLP) offers potential for automating this extraction process.

Purpose of the Study:

  • To systematically review the application of NLP techniques for extracting information related to substance use disorders.
  • To identify common NLP approaches, methods, software, and evaluation metrics used in SUD research.

Main Methods:

  • A systematic review of 623 studies was conducted, with 35 meeting the inclusion criteria.
  • Studies were analyzed based on the type of SUD addressed (alcohol, opioids, tobacco, multiple).
  • NLP approaches (Rule-Based, Machine Learning, Deep Learning), methods, software (Python, cTAKES), and evaluation metrics (F1, ROC AUC) were categorized.

Main Results:

  • The majority of included studies (65.7%) employed Rule-Based NLP approaches, with concept extraction being the most common method (43%).
  • Opioid-related SUDs (34.3%) and multiple SUDs (45.7%) were frequently studied.
  • Python emerged as the most utilized software (10 papers), followed by cTAKES (9 papers).
  • Evaluation metrics like ROC AUC and F1 scores demonstrated acceptable to outstanding performance.

Conclusions:

  • Rule-Based NLP methods, concept extraction, and common software like Python are prevalent in current SUD information extraction research.
  • Existing NLP applications achieve good performance in identifying SUD-related information.
  • Future research should explore advanced Machine Learning or Deep Learning approaches and specialized NLP software to enhance public health research and clinical decision-making.