ArticlePLOS digital health2022
An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records.
Article in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 41 citations in OpenAlex.
- Application of explainable artificial intelligence integrating with electronic health record in oncology.Exploration of targeted anti-tumor therapy · 2026Review
- Equity by Design - Using Digital Technology To Overcome Cardiovascular Health Disparities.Current cardiology reports · 2025Review
- Role of artificial intelligence in congenital heart disease.World journal of clinical pediatrics · 2025Review
- Genome sequencing is critical for forecasting outcomes following congenital cardiac surgery.Nature communications · 2025Observational
- Electronic Health Records: A Gateway to AI-Driven Multimorbidity Solutions-A Comprehensive Systematic Review.Journal of clinical medicine · 2025Review
- Explainable artificial intelligence for stroke risk stratification in atrial fibrillation.European heart journal. Digital health · 2025Review
- An Explainable AI Application (AF'fective) to Support Monitoring of Patients With Atrial Fibrillation After Catheter Ablation: Qualitative Focus Group, Design Session, and Interview Study.JMIR human factors · 2025Article
- AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.BMC pregnancy and childbirth · 2025Observational
- Article
- The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review.Journal of personalized medicine · 2024Review
- Probabilistic Graphical Models for Evaluating the Utility of Data-Driven ICD Code Categories in Pediatric Sepsis.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study.Frontiers in medicine · 2024Article
- An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis.PloS one · 2024Article
- A review of evaluation approaches for explainable AI with applications in cardiology.Artificial intelligence review · 2024Article
- Genetic and clinical variables act synergistically to impact neurodevelopmental outcomes in children with single ventricle heart disease.Communications medicine · 2023Article
- An Explainable Artificial Intelligence Approach for Discovering Social Determinants of Health and Risk Interactions for Stroke in Patients With Atrial Fibrillation.The American journal of cardiology · 2023Article
- American Heart Association's Children's Strategically Focused Research Network Experience.Journal of the American Heart Association · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 2 institutions in 1 country.
Funding
Abstract
Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.