Evidence map›Paper›PMID 39815256›Full record

ArticleBMC medical informatics and decision making2025

Causal analysis for multivariate integrated clinical and environmental exposures data.

Meghamala Sinha, Perry Haaland, Ashok Krishnamurthy, Bo Lan, Stephen A Ramsey, Patrick L Schmitt, Priya Sharma, Hao Xu, Karamarie Fecho

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Meghamala SinhaSchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, 97331, USA. meghamala.sinha@gmail.com.
Perry HaalandDepartment of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Ashok KrishnamurthyRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Bo LanUNC Highway Safety Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Stephen A RamseySchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, 97331, USA.
Patrick L SchmittRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Priya SharmaRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Hao XuRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Karamarie FechoRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Funding

North Carolina Translational and Clinical Sciences Institute (NC TraCS)UM1TR004406 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI NICHOLAS J SHAHEEN · 2023 to 2026
$37.5M
ICEES+ Knowledge Provider: Leveraging Open Clinical and Environmental Data to Accelerate and Drive Innovation in Translational Research and Clinical Care.OT2TR003430 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI AHALT, STANLEY CARLTON, KRISHNAMURTHY, ASHOK KUMAR · 2020 to 2024
$4.4M
NCATS NIH HHS OT2 TR003430NCATS NIH HHS UM1 TR004406NIH National Institutes of Health & NCATS National Center for Advancing Translational Sciences OT2-TR003428NIH National Institutes of Health & NCATS National Center for Advancing Translational Sciences OT2-TR003430NIH National Institutes of Health & NCATS National Center for Advancing Translational Sciences OT3-TR002020NIH National Institutes of Health & NCATS National Center for Advancing Translational Sciences UL1-TR002489NIH National Institutes of Health & NCATS National Center for Advancing Translational Sciences UL1-TR002489-03S4
6 · The paper itself

Abstract

Electronic health records (EHRs) provide a rich source of observational patient data that can be explored to infer underlying causal relationships. These causal relationships can be applied to augment medical decision-making or suggest hypotheses for healthcare research. In this study, we explored a large-scale EHR dataset on patients with asthma or related conditions (N = 14,937). The dataset included integrated data on features representing demographic factors, clinical measures, and environmental exposures. The data were accessed via a service named the Integrated Clinical and Environmental Service (ICEES). We estimated underlying causal relationships from the data to identify significant predictors of asthma attacks. We also performed simulated interventions on the inferred causal network to detect the causal effects, in terms of shifts in probability distribution for asthma attacks.

Indexed as

AsthmaCausalityElectronic Health RecordsEnvironmental ExposureAdultFemaleHumansMaleMiddle AgedAsthmaCausal inferenceOpen clinical dataStructure learning

Identifiers

PMID39815256
PMCPMC11736916

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.