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A machine learning driven automated system for safety data sheet indexing.

Aatish Suman1, Misbah Khan2, Veeru Talreja2

  • 1Velocity EHS Inc, Chicago, IL, 60654, USA. asuman@ehs.com.

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|February 22, 2024
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Summary
This summary is machine-generated.

Automating Safety Data Sheet (SDS) indexing extracts crucial chemical composition data. This new system efficiently identifies ingredient names, CAS numbers, and percentages from SDS documents, reducing manual effort.

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Area of Science:

  • Chemical Safety and Management
  • Data Science and Machine Learning
  • Environmental Health and Safety (EHS)

Background:

  • Safety Data Sheets (SDS) are essential for chemical management, EHS, and ESG compliance.
  • Manual SDS indexing is labor-intensive, costly, and time-consuming.
  • Structured data extraction from SDS is vital for efficient information retrieval.

Purpose of the Study:

  • To develop an automated system for indexing critical information from SDS documents.
  • To extract ingredient names, Chemical Abstracts Service (CAS) numbers, and weight percentages.
  • To replace traditional manual SDS indexing with an efficient, automated solution.

Main Methods:

  • A multi-stage ensemble method combining machine learning models and rule-based systems.
  • Input: SDS documents in PDF format.
  • Output: Tabular data of ingredient names, CAS numbers, and weight percentages.

Main Results:

  • The automated system successfully indexes composition information from SDS.
  • Achieved a document-level precision of 0.93.
  • Validated on a dataset of 20,000 annotated SDS documents.

Conclusions:

  • The developed automated system offers an efficient and accurate method for SDS indexing.
  • This approach significantly improves the extraction of chemical composition data.
  • The system supports enhanced chemical management, EHS, and ESG practices.