Details
| Original language | English |
|---|---|
| Title of host publication | Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV |
| Subtitle of host publication | Special Issue on Data Management - Principles, Technologies, and Applications |
| Editors | Abdelkader Hameurlain, A Min Tjoa, Omar Boucelma, Farouk Toumani |
| Pages | 89-118 |
| Number of pages | 30 |
| ISBN (electronic) | 978-3-662-68014-8 |
| Publication status | Published - 2023 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 14160 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Keywords
- Healthcare Data Analytics, Knowledge Graphs, Semantic Data Integration
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Sustainable Development Goals
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Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV: Special Issue on Data Management - Principles, Technologies, and Applications. ed. / Abdelkader Hameurlain; A Min Tjoa; Omar Boucelma; Farouk Toumani. 2023. p. 89-118 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14160 LNCS).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Challenges for Healthcare Data Analytics Over Knowledge Graphs
AU - Vidal, Maria-Esther
AU - Niazmand, Emetis
AU - Rohde, Philipp D.
AU - Iglesias, Enrique
AU - Sakor, Ahmad
N1 - Funding Information: 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.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Healthcare Data Analytics
KW - Knowledge Graphs
KW - Semantic Data Integration
UR - http://www.scopus.com/inward/record.url?scp=85174618837&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-68014-8_4
DO - 10.1007/978-3-662-68014-8_4
M3 - Contribution to book/anthology
SN - 978-3-662-68013-1
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 89
EP - 118
BT - Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV
A2 - Hameurlain, Abdelkader
A2 - Tjoa, A Min
A2 - Boucelma, Omar
A2 - Toumani, Farouk
ER -