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Use of deep learning-based NLP models for full-text data elements extraction for systematic literature review tasks.

Jingcheng Du1, Dong Wang2, Bin Lin1

  • 1Intelligent Medical Objects, Houston, TX, USA.

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Summary
This summary is machine-generated.

This study shows that deep learning models, especially Long Short-Term Memory (LSTM), can automatically extract data for Health Economics and Outcomes Research (HEOR) systematic literature reviews (SLRs). These NLP approaches improve efficiency in synthesizing HEOR evidence.

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Area of Science:

  • Health Economics and Outcomes Research (HEOR)
  • Natural Language Processing (NLP)
  • Machine Learning

Background:

  • Systematic literature reviews (SLRs) are crucial for HEOR evidence synthesis but are labor-intensive.
  • Previous work developed machine learning for identifying relevant publications.
  • This study explores NLP for automated data extraction from scientific literature.

Purpose of the Study:

  • To investigate the feasibility of using NLP for automated data element extraction in SLRs.
  • To compare the performance of different NLP algorithms (CRF, LSTM, BERT) for this task.
  • To provide annotated corpora for the NLP community.

Main Methods:

  • Collected and annotated 239 full-text articles for 12 variables across three HEOR topics: HPV Prevalence, Pneumococcal Epidemiology, and Pneumococcal Economic Burden.
  • Trained and evaluated Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) models.
  • Shared three annotated corpora publicly as a benchmark.

Main Results:

  • Deep learning algorithms outperformed conventional machine learning for SLR data element extraction.
  • LSTM models achieved superior micro-averaged F1 scores (0.890, 0.646, 0.615) across the three tasks.
  • CRF models showed limited performance, and BERT did not yield expected improvements in this specific context.

Conclusions:

  • Deep learning, particularly LSTM, demonstrates superior performance for automated data extraction in HEOR SLRs.
  • LSTM models are recommended for deployment due to their performance, generalizability, scalability, and cost-effectiveness.
  • The shared corpora will facilitate further NLP research in SLR evidence synthesis.