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TREXMO plus: an advanced self-learning model for occupational exposure assessment.

Nenad Savic1, Eun Gyung Lee2, Bojan Gasic3

  • 1Center for Primary Care and Public Health (Unisanté), University of Lausanne, Route de la Corniche 2, CH-1066, Epalinges-Lausanne, Switzerland. nenad.savic@unisante.ch.

Journal of Exposure Science & Environmental Epidemiology
|February 5, 2020
PubMed
Summary

A new approach, TREXMO+, combines three occupational exposure models using machine learning. TREXMO+ offers more accurate risk assessment predictions than individual models, improving regulatory exposure assessments.

Keywords:
Advanced REACH toolECETOC TRAExposure assessmentREACHStoffenmanagerTREXMO

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Area of Science:

  • Occupational hygiene
  • Risk assessment
  • Computational toxicology

Background:

  • Occupational exposure models are vital for regulatory risk assessment in Europe.
  • Existing models like Advanced REACH Tool, Stoffenmanger, and ECETOC TRAv3 have limitations in prediction accuracy and model selection.
  • There is a need for improved methods to integrate and enhance the reliability of these exposure models.

Purpose of the Study:

  • To develop an advanced modeling approach, TREXMO+, integrating multiple popular occupational exposure models.
  • To improve the accuracy and reduce bias in exposure predictions for regulatory risk characterization.
  • To establish clear rules for selecting the best-fit model under various workplace conditions.

Main Methods:

  • Developed TREXMO+, an extension of the TREXMO tool, utilizing machine learning.
  • TREXMO+ integrates predictions from Advanced REACH Tool, Stoffenmanger, and ECETOC TRAv3.
  • Employed a machine-learning technique to segment exposure data into condition-specific subsets and build regression models for each.

Main Results:

  • TREXMO+ demonstrated significantly less bias and higher accuracy compared to individual conventional models.
  • TREXMO+ predictions typically differ from measurements by a factor of 2-3.
  • Conventional models showed a larger deviation from measurements, with factors ranging from 2-14.

Conclusions:

  • The TREXMO+ approach offers a more reliable and accurate method for occupational exposure assessment.
  • This integrated modeling strategy enhances regulatory risk characterization by providing better exposure predictions.
  • Further validation with larger datasets is recommended to confirm the robust performance of TREXMO+.