Paul Rogers Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Biostatistician

National Center for Toxicological Research

faculty

9 h-index 89 pubs 319 cited

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Biography and Research Information

OverviewAI-generated summary

Paul Rogers is a biostatistician at the National Center for Toxicological Research. His research interests include the application of artificial intelligence and data mining techniques to analyze large-scale health databases. He has investigated opioid-related adverse events submitted to the FDA Adverse Events Reporting System (FAERS) and utilized the Medical Information Mart for Intensive Care (MIMIC) database to study opioid poisoning. Rogers has also explored sex-based differences in the immunotoxicity of nanoparticles and developed health indices for specific populations, such as the Charlson Comorbidity Index for the American Indian population. His work leverages advanced statistical methods and computational approaches to identify patterns and risks within real-world health data.

Metrics

  • h-index: 9
  • Publications: 89
  • Citations: 319

Selected Publications

  • AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women (2025) DOI
  • Leveraging AI to improve disease screening among American Indians: insights from the Strong Heart Study (2025) DOI
  • Developing a Charlson Comorbidity Index for the American Indian Population Using the Epidemiologic Data from the Strong Heart Study (2024) DOI
  • Investigation of sex-based differences in the immunotoxicity of silver nanoparticles (2024) DOI
  • A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database (2023) DOI
  • Developing a Charlson Comorbidity Index for the American Indian Population Using the Epidemiologic Data from the Strong Heart Study (2023) DOI
  • Identifying Vulnerabilities to NSAID Adverse Events in the US Population: An Analysis of Pre-Existing Conditions and Sex (2023) DOI
  • Controlling for Confounding in Complex Survey Machine Learning Models to Assess Drug Safety and Risk (2023) DOI
  • Assessment of a Modified Sandwich Estimator for Generalized Estimating Equations with Application to Opioid Poisoning in MIMIC-IV ICU Patients (2021) DOI
  • Medical Information Mart for Intensive Care: A Foundation for the Fusion of Artificial Intelligence and Real-World Data (2021) DOI

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