Artificial intelligence exceeds humans in epidemiological job coding.

ABSTRACT:

Background

Work circumstances can substantially negatively impact health. To explore this, large occupational cohorts of free-text job descriptions are manually coded and linked to exposure. Although several automatic coding tools have been developed, accurate exposure assessment is only feasible with human intervention.

Methods

We developed OPERAS, a customizable decision support system for epidemiological job coding. Using 812,522 entries, we developed and tested classification models for the Professions et Catégories Socioprofessionnelles (PCS)2003, Nomenclature d'Activités Française (NAF)2008, International Standard Classifications of Occupation (ISCO)-88, and ISCO-68. Each code comes with an estimated correctness measure to identify instances potentially requiring exper

SUBMITTER: Langezaal MA 

PROVIDER: S-EPMC10625577 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature