Evidence map›Paper›PMID 36120162›Full record

ReviewOnline journal of public health informatics2022

The Representation of Causality and Causation with Ontologies: A Systematic Literature Review.

Suhila Sawesi, Mohamed Rashrash, Olaf Dammann

Abstract readReview
In one paragraph

Review in Online journal of public health informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

3 authors.

Suhila SawesiDept. of Health Informatics and Bioinformatics, Grand Valley State University, School of Computing, MI, USA.
Mohamed RashrashDept. of Pharmaceutical and Administrative Sciences, University of Charleston, School of Pharmacy, Charleston, WV, USA.
Olaf DammannDept. of Health Informatics and Bioinformatics, Grand Valley State University, School of Computing, MI, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To explore how disease-related causality is formally represented in current ontologies and identify their potential limitations. Methods: We conducted a systematic literature search on eight databases (PubMed, Institute of Electrical and Electronic Engendering (IEEE Xplore), Association for Computing Machinery (ACM), Scopus, Web of Science databases, Ontobee, OBO Foundry, and Bioportal. We included studies published between January 1, 1970, and December 9, 2020, that formally represent the notions of causality and causation in the medical domain using ontology as a representational tool. Further inclusion criteria were publication in English and peer-reviewed journals or conference proceedings. Two authors (SS, RM) independently assessed study quality and performed content analysis using a modified validated extraction grid with pre-established categorization. Results: The search strategy led to a total of 8,501 potentially relevant papers, of which 50 met the inclusion criteria. Only 14 out of 50 (28%) specified the nature of causation, and only 7 (14%) included clear and non-circular natural language definitions. Although several theories of causality were mentioned, none of the articles offers a widely accepted conceptualization of how causation and causality can be formally represented. Conclusion: No current ontology captures the wealth of available concepts of causality. This provides an opportunity for the development of a formal ontology of causation/causality.

Indexed as

causalitycausationknowledge representationontology

Identifiers

PMID36120162
PMCPMC9473331

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.