Knowledge Graphs for Enhancing Transparency in Health Data Ecosystems

Submitted by Maria-Esther Vidal on 10/24/2022 - 09:23

Tracking #: 3294-4508

Authors: 
Fotis Aisopos
Samaneh Jozashoori2
Emetis Niazmand
Disha Purohit
Ariam Rivas
Ahmad Sakor
Enrique Iglesias
Dimitrios Vogiatzis1
Ernestina Menasalvas
Alejandro Rodriguez Gonzalez
Guillermo Vigueras
Daniel Gomez-Bravo
Maria Torrente
Roberto Lopez
Mariano Provencio Pulla
Athanasios Dalianis
Ana Triantafillou
Georgios Paliouras
Maria-Esther Vidal

Responsible editor: 
Guest Editors SW Meets Health Data Management 2022

Submission type: 
Full Paper
Abstract: 
Tailoring personalized treatments demands the analysis of a patient's characteristics, which may be scattered over a wide variety of sources. These features include family history, life habits, comorbidities, and potential treatment side effects. Moreover, the analysis of the services visited the most by a patient before a new diagnosis and the type of requested tests, may uncover patterns that contribute to earlier disease detection and treatment effectiveness. Built on the concept of knowledge-driven ecosystems, we devise DE4LungCancer, a data ecosystem of health data sources for lung cancer. Knowledge extracted from heterogeneous sources, e.g., clinical records, scientific publications, and pharmacologic data, is integrated into knowledge graphs. Ontologies describe the meaning of the combined data, and mapping rules enable the declarative definition of the transformation and integration processes. Moreover, DE4LungCancer is assessed in terms of the methods followed for data quality assessment and curation. Lastly, the role of controlled vocabularies and ontologies in health data management is discussed and their impact on transparent knowledge extraction and analytics. This paper presents the lesson learned in the DE4LungCancer development and demonstrates the transparency level supported by the proposed knowledge-driven ecosystem in the context of the lung cancer pilots in the EU H2020 funded project BigMedilytic, the ERA PerMed funded project P4-LUCAT, and the EU H2020 projects CLARIFY and iASiS.
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Previous Version: 
Knowledge Graphs for Enhancing Transparency in Health Data Ecosystems
Tags: 
Reviewed
Long-term Stable Link to Resources: 
https://github.com/SDM-TIB/DE4LC

Decision/Status: 
Accept

Solicited Reviews:
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Review #1
By Sara Colantonio submitted on 01/Dec/2022
Suggestion:
Accept
Review Comment:

The authors have suitably addressed the reviewers' comments

This manuscript was submitted as 'full paper' and should be reviewed along the usual dimensions for research contributions which include (1) originality, (2) significance of the results, and (3) quality of writing. Please also assess the data file provided by the authors under “Long-term stable URL for resources”. In particular, assess (A) whether the data file is well organized and in particular contains a README file which makes it easy for you to assess the data, (B) whether the provided resources appear to be complete for replication of experiments, and if not, why, (C) whether the chosen repository, if it is not GitHub, Figshare or Zenodo, is appropriate for long-term repository discoverability, and (4) whether the provided data artifacts are complete. Please refer to the reviewer instructions and the FAQ for further information.

Review #2
By Stelios Sfakiannakis submitted on 02/Dec/2022
Suggestion:
Accept
Review Comment:

This manuscript was submitted as 'full paper' and should be reviewed along the usual dimensions for research contributions which include (1) originality, (2) significance of the results, and (3) quality of writing. Please also assess the data file provided by the authors under “Long-term stable URL for resources”. In particular, assess (A) whether the data file is well organized and in particular contains a README file which makes it easy for you to assess the data, (B) whether the provided resources appear to be complete for replication of experiments, and if not, why, (C) whether the chosen repository, if it is not GitHub, Figshare or Zenodo, is appropriate for long-term repository discoverability, and (4) whether the provided data artifacts are complete. Please refer to the reviewer instructions and the FAQ for further information.