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 -