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Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.

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Summary
This summary is machine-generated.

This review explores how Natural Language Processing (NLP) structures valuable information within Clinical Data Warehouses (CDWs). NLP enhances clinical research by extracting and transforming data, though challenges remain, particularly with non-English texts.

Keywords:
AIartificial intelligenceclinical data warehousedata warehousingnatural language processing

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Area of Science:

  • Biomedical Informatics
  • Computational Linguistics
  • Health Data Science

Background:

  • Clinical Data Warehouses (CDWs) enable secondary use of health data from clinical care.
  • Unstructured clinical text contains significant, high-value information.
  • Natural Language Processing (NLP) can structure and improve accessibility of this textual data.

Purpose of the Study:

  • To review studies applying NLP to textual data from CDWs.
  • To identify common NLP tasks performed on CDW data.
  • To categorize the NLP methods employed for these tasks.

Main Methods:

  • Systematic review following PRISMA guidelines.
  • Searched PubMed, Google Scholar, and ACL Anthology databases.
  • Included English articles from 1995-2021 focusing on NLP and CDW textual data.

Main Results:

  • 194 out of 1353 articles met inclusion criteria.
  • Information extraction (57.7%) and patient identification (26.3%) were primary NLP tasks.
  • Symbolic methods (53.4%) were most common, followed by machine learning (30.2%) and deep learning (16.4%).
  • Most NLP applications focused on English language data (78.9%).

Conclusions:

  • CDWs are crucial for secondary use of clinical text in research.
  • Clinical NLP effectively accesses, extracts, and transforms CDW data.
  • Challenges persist, especially for non-English languages, but NLP impacts clinical research and practice.