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Contextualizing injury severity from occupational accident reports using an optimized deep learning prediction model.

Mohamed Zul Fadhli Khairuddin1, Suresh Sankaranarayanan2, Khairunnisa Hasikin3

  • 1Institute of Medical Science Technology, Universiti Kuala Lumpur, Kajang, Selangor, Malaysia.

Peerj. Computer Science
|April 25, 2024
PubMed
Summary

This study uses deep learning to predict occupational injury severity from narratives. The optimized Bidirectional Long Short-Term Memory (Bi-LSTM) model achieved high accuracy, offering insights for workplace safety.

Keywords:
Deep learningMachine learningNatural language processingOccupational injuryOccupational safety and healthText classification

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Area of Science:

  • Occupational Safety and Health
  • Data Science
  • Machine Learning

Background:

  • Conventional occupational injury assessment relies on structured data.
  • Unstructured injury narratives contain rich, underutilized information.
  • A novel deep learning approach is proposed to analyze these narratives.

Purpose of the Study:

  • To develop and validate a deep learning model for predicting occupational injury severity.
  • To extract actionable insights from unstructured injury narratives.
  • To enhance occupational injury assessment and proactive safety measures.

Main Methods:

  • Utilized Natural Language Processing (NLP) on US Occupational Safety and Health Administration (OSHA) data (2015-2023).
  • Applied Term Frequency-Inverse Document Frequency (TF-IDF) and Global Vector (GloVe) embeddings.
  • Developed and optimized a Bidirectional Long Short-Term Memory (Bi-LSTM) model with feature importance analysis.

Main Results:

  • Optimized Bi-LSTM model achieved high accuracy: 0.95 for hospitalization, 0.98 for amputation.
  • Demonstrated faster model processing times compared to traditional classifiers.
  • Feature importance analysis identified keywords linked to injury causes, enhancing interpretability.

Conclusions:

  • The optimized Bi-LSTM model provides an effective tool for improving workplace safety.
  • Findings support proactive safety measures, contributing to business productivity and sustainability.
  • Establishes a foundation for predictive analytics in occupational safety and health.