%0 Journal Article %1 vidal2023challenges %A Vidal, Maria-Esther %A Niazmand, Emetis %A Rohde, Philipp D. %A Iglesias, Enrique %A Sakor, Ahmad %D 2023 %J Trans. Large Scale Data Knowl. Centered Syst. %K myown %P 89-118 %R https://doi.org/10.1007/978-3-662-68014-8_4 %T Challenges for Healthcare Data Analytics Over Knowledge Graphs %V 54 %X 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.

@article{vidal2023challenges, 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.}, added-at = {2024-02-15T15:45:43.000+0100}, author = {Vidal, Maria-Esther and Niazmand, Emetis and Rohde, Philipp D. and Iglesias, Enrique and Sakor, Ahmad}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://www.bibsonomy.org/bibtex/2fd60297a0a15744f65e9555b8c088629/gabydler}, doi = {https://doi.org/10.1007/978-3-662-68014-8_4}, interhash = {f354f2a39ecf2e99b7c368ddede5cb3d}, intrahash = {fd60297a0a15744f65e9555b8c088629}, journal = {Trans. Large Scale Data Knowl. Centered Syst.}, keywords = {myown}, pages = {89-118}, timestamp = {2024-02-15T17:17:45.000+0100}, title = {Challenges for Healthcare Data Analytics Over Knowledge Graphs}, volume = 54, year = 2023 }