Evidence map›Paper›PMID 40954476›Full record

ArticleJournal of biomedical semantics2025

Three-layered semantic framework for public health intelligence.

Sathvik Guru Rao, Pranitha Rokkam, Bide Zhang, Astghik Sargsyan, Abish Kaladharan, Priya Sethumadhavan, Marc Jacobs, Martin Hofmann-Apitius, Alpha Tom Kodamullil

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Article in Journal of biomedical semantics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

9 authors.

Sathvik Guru RaoDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany. sathvik.guru.rao@scai.fraunhofer.de.
Pranitha RokkamDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany.
Bide ZhangDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany.
Astghik SargsyanDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany.
Abish KaladharanCausality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin, 683503, Kerala, India.
Priya SethumadhavanCausality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin, 683503, Kerala, India.
Marc JacobsDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany.
Martin Hofmann-ApitiusDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany.
Alpha Tom KodamullilDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany. alpha.tom.kodamullil@scai.fraunhofer.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDisease surveillance systems play a crucial role in monitoring and preventing infectious diseases. However, the current landscape, primarily focused on fragmented health data, poses challenges to contextual understanding and decision-making. This paper addresses this issue by proposing a semantic framework using ontologies to provide a unified data representation for seamless integration. The paper demonstrates the effectiveness of this approach using a case study of a COVID-19 incident at a football game in Italy.

methodIn this study, we undertook a comprehensive approach to gather and analyze data for the development of ontologies within the realm of pandemic intelligence. Multiple ontologies were meticulously crafted to cater to different domains related to pandemic intelligence, such as healthcare systems, mass gatherings, travel, and diseases. The ontologies were classified into top-level, domain, and application layers. This classification facilitated the development of a three-layered architecture, promoting reusability, and consistency in knowledge representation, and serving as the backbone of our semantic framework.

resultThrough the utilization of our semantic framework, we accomplished semantic enrichment of both structured and unstructured data. The integration of data from diverse sources involved mapping to ontology concepts, leading to the creation and storage of RDF triples in the triple store. This process resulted in the construction of linked data, ultimately enhancing the discoverability and accessibility of valuable insights. Furthermore, our anomaly detection algorithm effectively leveraged knowledge graphs extracted from the triple store, employing semantic relationships to discern patterns and anomalies within the data. Notably, this capability was exemplified by the identification of correlations between a football game and a COVID-19 event occurring at the same location and time.

conclusionThe framework showcased its capability to address intricate, multi-domain queries and support diverse levels of detail. Additionally, it demonstrated proficiency in data analysis and visualization, generating graphs that depict patterns and trends; however, challenges related to ontology maintenance, alignment, and mapping must be addressed for the approach's optimal utilization.

Indexed as

Biological OntologiesPublic HealthPublic Health InformaticsSemanticsCOVID-19HumansItalyPandemicsSARS-CoV-2Data integrationData interoperabilityOntologyPublic health intelligenceSemantic frameworkSemantic webWeb of data

Identifiers

PMID40954476
PMCPMC12439389

What OpenQuestion holds

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LicenceCC BY
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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.