Electronic Health Record (EHR) Enhanced Signal Detection Using Tree-Based Scan Statistic Methods

Abstract

Tree-based scan statistics (TBSS) are data mining methods that screen thousands of hierarchically related health outcomes to detect unsuspected adverse drug effects. TBSS traditionally analyze claims data with outcomes defined via diagnosis codes. TBSS have not been previously applied to rich clinical information in electronic health records (EHR). We developed approaches for integrating EHR data in TBSS analyses, including outcomes derived from natural language processing (NLP) applied to clinical notes and laboratory results, related via multipath hierarchical structures. We consider 4 settings that sequentially add sources of outcomes to the TBSS tree: (1) diagnosis code, (2) NLP-derived outcomes, (3) binary outcomes from lab results, and (4) continuous lab results. In a comparative cohort study involving second-generation sulfonylureas (SUs) and dipeptidyl peptidase 4 (DPP-4) inhibitors among adults with type 2 diabetes, with an a priori expected signal of hypoglycemia, diagnosis code data showed no statistical alerts for inpatient or emergency department settings. Adding NLP-derived outcomes resulted in an alert for “Headaches” (P = .047), a nonspecific symptom of hypoglycemia. Progressively adding binary and continuous lab results produced the same alert. Integrating EHR in TBSS can be useful for the detection of safety signals for further investigation.

Contribution highlights

• Tree-based scan statistics (TBSS) are data mining methods that screen thousands of hierarchically related health outcomes to detect unsuspected adverse drug effects.
• TBSS traditionally analyze claims data with outcomes defined via diagnosis codes.

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

Code and data

Publication metadata

DOI

10.1093/aje/kwaf199

PMID

40916726

Volume

195

Issue

1

Pages / article

178-187

Version relationship

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

BibTeX

@article{Russo2025Electronichealth,
  title = {Electronic Health Record (EHR) Enhanced Signal Detection Using Tree-Based Scan Statistic Methods},
  author = {Massimiliano Russo and Sushama Kattinakere Sreedhara and Joshua Smith and Sharon E Davis and Judith C Maro and Thomas Deramus and Joyce Lii and Jie Yang and Rishi Desai and José J Hernández-Muñoz},
  journal = {American Journal of Epidemiology},
  year = {2025},
  doi = {10.1093/aje/kwaf199},
  pmid = {40916726},
  volume = {195},
  number = {1},
  pages = {178--187}
}