AI Article Synopsis
- The study aimed to compare maternal demographics based on how occupations were coded (automatically vs. manually) and to check for bias from excluding manually coded jobs.
- Using a case-control design, cases of clefts were examined from the Texas Birth Defects Registry, with a focus on how occupations were coded and their relationship to birth defects.
- The results showed that automatic coding was effective for over 90% of cases and identified specific occupations linked to clefts, indicating that machine learning can be useful in researching occupational links to birth defects.
Article Abstract
Objective: To compare maternal demographics based on occupation coding status and evaluate potential bias by excluding manually coded occupations.
Methods: This case-control study assessed cases with clefts obtained from the Texas Birth Defects Registry. The NIOSH Industry and Occupation Computerized Coding System automatically coded occupations, with manual coding for unclassified cases. Maternal demographics were tabulated by occupation coding status (manual vs. automatic). Logistic regression examined associations between major occupation groups and clefts.
Results: Automatic coding covered over 90% of all mothers. Building, grounds cleaning, and maintenance occupations, and office and administrative support occupations were significantly associated with cleft lip with or without cleft palate, even after excluding manually coded occupations.
Conclusion: We found consistent associations before and after excluding manually coded data for most comparisons, suggesting that machine learning can facilitate occupation-related birth defects research.
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| http://dx.doi.org/10.1097/JOM.0000000000003189 | DOI Listing |
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