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Updated: Sep 28, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health

Sergiusz Wesołowski1, Gordon Lemmon1, Edgar J Hernandez1

  • 1Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America.

PLOS Digital Health
|April 4, 2022
PubMed

Insights

This study introduces a new method to discover comorbidities from electronic health records (EHRs). It reveals how these conditions and patient demographics impact cardiovascular health, creating valuable research tools.

Area of Science:

  • Computational biology
  • Medical informatics
  • Cardiovascular research

Background:

  • Precision medicine requires understanding complex clinical variables affecting cardiovascular health.
  • Electronic Health Records (EHRs) contain vast data but are challenging to analyze for comorbidities.

Purpose of the Study:

  • To apply a novel comorbidity discovery method to large-scale EHR data.
  • To identify and analyze the impact of comorbidities and demographics on cardiovascular outcomes.
  • To develop portable, privacy-preserving tools for EHR-based research.

Main Methods:

  • Utilized Poisson Binomial based Comorbidity discovery (PBC), a scalable method for comorbidity analysis.
  • Analyzed EHR data from over 1.6 million patients across 77 million visits.
  • Employed explainable Artificial Intelligence (AI) to dissect conditional dependencies between comorbidities, demographics, and cardiovascular health.

Main Results:

  • Discovered complex multimorbidity networks associated with cardiovascular conditions.
  • Identified key comorbid conditions and demographic factors influencing heart transplant, sinoatrial node dysfunction, and congenital heart disease.
  • Developed web-based tools for exploring comorbid and demographic landscapes of cardiovascular outcomes.

Conclusions:

  • The PBC method effectively transforms large EHR datasets into accessible, privacy-preserving research tools.
  • This approach facilitates deeper understanding of multimorbidity and its impact on cardiovascular health.
  • The developed tools support community-based outcomes research and advance precision medicine.

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