Natural Language Processing for Scalable Feature Engineering and Ultra-High-Dimensional Confounding Adjustment in Healthcare Database Studies

Abstract

Background To improve confounding control in healthcare database studies, data-driven algorithms may empirically identify and adjust for large numbers of pre-exposure variables that indirectly capture information on unmeasured confounding factors ('proxy' confounders). Current approaches for high-dimensional proxy adjustment do not leverage free-text notes from electronic health records (EHRs). Unsupervised natural language processing (NLP) technology can scale to generate large numbers of structured features from unstructured notes. Objective To assess the impact of supplementing claims data analyses with large numbers of NLP generated features for high-dimensional proxy adjustment. Methods We linked Medicare claims with EHR data to generate three cohorts comparing different classes of medications on the 6-month risk of cardiovascular outcomes. We used various NLP methods to generate structured features from free-text EHR notes and used least absolute shrinkage and selection operator (LASSO) regression to fit several propensity score (PS) models that included different covariate sets as candidate predictors. Covariate sets included features generated from claims data only, and claims data plus NLP-generated EHR features. Results Including both claims codes and NLP-generated EHR features as candidate predictors improved overall covariate balance with standardized differences being Conclusion Supplementing administrative claims with large numbers of NLP-generated features for ultra-high-dimensional proxy confounder adjustment improved overall covariate balance and may provide a modest benefit in terms of capturing confounder information.

Contribution highlights

• Background To improve confounding control in healthcare database studies, data-driven algorithms may empirically identify and adjust for large numbers of pre-exposure variables that indirectly capture information on unmeasured confounding factors ('proxy' confounders).
• Current approaches for high-dimensional proxy adjustment do not leverage free-text notes from electronic health records (EHRs).

Extracted from the public abstract; no claims have been added.

Code and data

Publication metadata

DOI

10.1016/j.jbi.2025.104882

PMID

40691893

Volume

169

Pages / article

104882

Version relationship

No separate preprint-to-publication relationship is currently recorded.

BibTeX

@article{Wyss2025Naturallanguage,
  title = {Natural Language Processing for Scalable Feature Engineering and Ultra-High-Dimensional Confounding Adjustment in Healthcare Database Studies},
  author = {Richard Wyss and Jie Yang and Sebastian Schneeweiss and Joseph M Plasek and Li Zhou and Thomas Deramus and Janick G Weberpals and Kerry Ngan and Kueiyu Joshua Lin},
  journal = {Journal of Biomedical Informatics},
  year = {2025},
  doi = {10.1016/j.jbi.2025.104882},
  pmid = {40691893},
  volume = {169},
  pages = {104882}
}