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Challenges for Healthcare Data Analytics Over Knowledge Graphs

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Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 14160))

Abstract

Over the past decade, the volume of data has experienced a significant increase, and this growth is projected to accelerate in the coming years. Within the healthcare sector, various methods (such as liquid biopsies, medical images, and genome sequencing) generate substantial amounts of data, which can lead to the discovery of new biomarkers. Analyzing big data in healthcare holds the potential to advance precise diagnostics and effective treatments. However, healthcare data faces several complexity challenges, including volume, variety, and veracity, which necessitate innovative techniques for data management and knowledge discovery to ensure accurate insights and informed decision-making. This paper summarizes the results presented in the invited talk at BDA 2022 and addresses these challenges by proposing a knowledge-driven framework able to handle complexity issues associated with big data and their impact on analytics. In particular, we propose the use of Knowledge Graphs (KGs) as data structures that enable the integration of diverse healthcare data and facilitate the merging of data with ontologies that describe their meaning. We show the benefits of leveraging KGs to uncover patterns and associations among entities. Specifically, we illustrate the application of rule mining tasks that enhance the understanding of the role of biomarkers and previous cancers in lung cancer.

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Notes

  1. 1.

    Unified Medical Language System https://www.nlm.nih.gov/research/umls/index.html.

  2. 2.

    https://www.w3.org/TR/shacl/.

  3. 3.

    https://www.w3.org/TR/vocab-dcat-3/.

  4. 4.

    https://www.clarify2020.eu/.

  5. 5.

    https://go.drugbank.com/.

  6. 6.

    https://www.ncbi.nlm.nih.gov/mesh/.

  7. 7.

    https://labs.tib.eu/sdm/clarify_mappings_and_ontology/sparql.

  8. 8.

    https://www.ontotext.com/products/graphdb/.

  9. 9.

    https://rdf4j.org/documentation/programming/federation/.

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Acknowledgement

This work has been supported by the EU H2020 RIA project CLARIFY (GA No. 875160). Maria-Esther Vidal is partially supported by Leibniz Association in the program “Leibniz Best Minds: Programme for Women Professors”, project TrustKG-Transforming Data in Trustable Insights with grant P99/2020.

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Authors and Affiliations

  1. Leibniz University, Hannover, Germany

    Maria-Esther Vidal, Emetis Niazmand, Philipp D. Rohde & Enrique Iglesias

  2. TIB Leibniz Information Centre for Science and Technology, Hannover, Germany

    Maria-Esther Vidal, Emetis Niazmand & Philipp D. Rohde

  3. L3S Research Center, Hannover, Germany

    Maria-Esther Vidal, Enrique Iglesias & Ahmad Sakor

Authors
  1. Maria-Esther Vidal
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  2. Emetis Niazmand
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  3. Philipp D. Rohde
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  4. Enrique Iglesias
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  5. Ahmad Sakor
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Correspondence to Maria-Esther Vidal .

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Editors and Affiliations

  1. Paul Sabatier University, IRIT, Toulouse, France

    Abdelkader Hameurlain

  2. Technical University of Vienna, IFS, Vienna, Austria

    A Min Tjoa

  3. Aix-Marseille University, LIS, Marseille, France

    Omar Boucelma

  4. Université Clermont Auvergne, CNRS, LIMOS, Aubiere, France

    Farouk Toumani

Appendices

A Federated SPARQL Queries

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B Results Federated Query Engines

See Table 4.

Table 4. Execution Times of Federated Query Engines. avg is the average execution time of the query over 10 runs. stdev reports the standard deviation observed across the 10 runs. FedX (RDF4J) outperforms GraphDB in all queries. GraphDB times out for query Q6. DeTrusty has the best performance of all three engines in all nine queries. Additionally, the query execution time of DeTrusty is the most stable one as can be seen by the low standard deviation.
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Vidal, ME., Niazmand, E., Rohde, P.D., Iglesias, E., Sakor, A. (2023). Challenges for Healthcare Data Analytics Over Knowledge Graphs. In: Hameurlain, A., Tjoa, A.M., Boucelma, O., Toumani, F. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV. Lecture Notes in Computer Science(), vol 14160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-68014-8_4

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