Evidence map›Paper›PMID 42828809›Full record

ArticleJACC. Advances2026

A Knowledge Graph Framework for Longitudinal Congenital Heart Disease Modeling: Methodology Corner.

Hang Xu, Lila Cunge, Zhengyang Ming, Siyan Chen, Majid Husain, Pierangelo Renella, Gentian Lluri, J Paul Finn, William Hsu, Kim-Lien Nguyen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Hang XuDivision of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles, Los Angeles, California, USA; Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA; Department of Bioengineering, University of California, Los Angeles, California, USA.
Lila CungeDivision of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles, Los Angeles, California, USA; Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA; Department of Bioengineering, University of California, Los Angeles, California, USA.
Zhengyang MingDivision of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles, Los Angeles, California, USA; Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA; Department of Bioengineering, University of California, Los Angeles, California, USA.
Siyan ChenDivision of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles, Los Angeles, California, USA; Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA; Department of Bioengineering, University of California, Los Angeles, California, USA.
Majid HusainDivision of Pediatric Cardiology and Mattel Children's Hospital, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Pierangelo RenellaDepartment of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA; University of California Irvine and Children's Hospital of Orange County, Irvine, California, USA.
Gentian LluriDivision of Cardiology and Ahmanson Adult Congenital Heart Disease Center, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
J Paul FinnDepartment of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA; Physics and Biology in Medicine Graduate Program, David Geffen School of Medicine at UCLA, Los Angeles, California, USA; Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
William HsuDepartment of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA; Department of Bioengineering, University of California, Los Angeles, California, USA.
Kim-Lien NguyenDivision of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles, Los Angeles, California, USA; Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA; Department of Bioengineering, University of California, Los Angeles, California, USA; Physics and Biology in Medicine Graduate Program, David Geffen School of Medicine at UCLA, Los Angeles, California, USA; Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA. Electronic address: klnguyen@ucla.edu.

Funding

UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
Expanding on a new paradigm for MRI in pediatric congenital heart diseaseR01HL127153 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHRISTODOULOU, ANTHONY G, FINN, JOHN PAUL · 2015 to 2024
$6.1M
NCATS NIH HHS UL1 TR001881NHLBI NIH HHS R01 HL127153
6 · The paper itself

Abstract

backgroundCongenital heart disease (CHD) is characterized by evolving clinical trajectories; staged interventions; and complex relationships among diagnoses, procedures, and encounters. Conventional machine learning applied to electronic health record (EHR) data often rely on static feature representations that do not preserve the natural longitudinal disease progression or clinical context.

objectivesThe purpose of this study was to develop a knowledge graph-based framework for trajectory-aware representation and analysis of longitudinal CHD EHR data.

methodsWe constructed a longitudinal knowledge graph that integrates patients, encounters, diagnoses, and procedures while preserving temporal ordering across the care continuum. Term frequency-inverse document frequency and Word2Vec approaches were applied to encounter contexts extracted from the graph to generate longitudinal patient representations that supported phenotyping, patient similarity analysis, and retrospective classification.

resultsUsing accumulated longitudinal EHR from a cardiovascular magnetic resonance cohort, we demonstrate that the framework captured clinically meaningful patterns and supported retrospective identification of single ventricle physiology as a representative use case.

conclusionsThis knowledge graph framework provides a scalable approach for preserving temporal and relational clinical context and enable trajectory-aware modeling of longitudinal cardiovascular EHR data in patients with CHD.

Indexed as

congenital heart diseaseelectronic health recordsknowledge graphpatient similarityTF-IDFWord2Vec

Identifiers

PMID42828809
PMCPMC13660652

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.