Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 106-111 |
| Number of pages | 6 |
| ISBN (electronic) | 9781728122861 |
| ISBN (print) | 978-1-7281-2287-8 |
| Publication status | Published - 2019 |
| Event | 32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019 - Cordoba, Spain Duration: 5 Jun 2019 → 7 Jun 2019 |
Publication series
| Name | Proceedings - IEEE Symposium on Computer-Based Medical Systems |
|---|---|
| Volume | 2019-June |
| ISSN (Print) | 1063-7125 |
Abstract
The vision of IASIS project is to turn the wave of big biomedical data heading our way into actionable knowledge for decision makers. This is achieved by integrating data from disparate sources, including genomics, electronic health records and bibliography, and applying advanced analytics methods to discover useful patterns. The goal is to turn large amounts of available data into actionable information to authorities for planning public health activities and policies. The integration and analysis of these heterogeneous sources of information will enable the best decisions to be made, allowing for diagnosis and treatment to be personalised to each individual. The project offers a common representation schema for the heterogeneous data sources. The iASiS infrastructure is able to convert clinical notes into usable data, combine them with genomic data, related bibliography, image data and more, and create a global knowledge base. This facilitates the use of intelligent methods in order to discover useful patterns across different resources. Using semantic integration of data gives the opportunity to generate information that is rich, auditable and reliable. This information can be used to provide better care, reduce errors and create more confidence in sharing data, thus providing more insights and opportunities. Data resources for two different disease categories are explored within the iASiS use cases, dementia and lung cancer.
Keywords
- Big data analysis, Dementia, Electronic health records, Genomics, Lung cancer, Personalized medicine
ASJC Scopus subject areas
- Medicine(all)
- Radiology Nuclear Medicine and imaging
- Computer Science(all)
- Computer Science Applications
Sustainable Development Goals
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Proceedings: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 106-111 8787467 (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2019-June).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - IASiS
T2 - 32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019
AU - Krithara, Anastasia
AU - Aisopos, Fotis
AU - Rentoumi, Vassiliki
AU - Nentidis, Anastasios
AU - Bougatiotis, Konstantinos
AU - Vidal, Maria Esther
AU - Menasalvas, Ernestina
AU - Rodriguez-Gonzalez, Alejandro
AU - Samaras, Eleftherios
AU - Garrard, Peter
AU - Torrente, Maria
AU - Provencio Pulla, Mariano
AU - Dimakopoulos, Nikos
AU - Mauricio, Rui
AU - De Argila, Jordi Rambla
AU - Tartaglia, Gian Gaetano
AU - Paliouras, George
N1 - Funding information: II. RELATED WORK Currently there are several efforts made by various international projects, which try to process and integrate various types of data towards the achievement of precision medicine. In specific Dementias Platform UK (DPUK)1is a public-private partnership funded by the Medical Research Council. DPUK brings data from multiple cohorts into the DPUK Data Portal. By joining data DPUK provides an integrated and collaborative environment, bringing together scientists from academia and industry to share knowledge and conduct joint research programs with the goal to fight to develop effective treatments for Dementia fast. The large number of individuals DPUK cohorts allows key research questions to be answered more rigorously and more rapidly than would otherwise be possible. In essence, DPUK involves the collection of various dementia related data sources ranging from HER (electronic health records), brain imaging and brain cell data. The DPUK platform encourages the development of new tools and resources to deliver research to accelerate pathways for future medicines. Moreover, TRACERx2(TRAcking Cancer Evolution through therapy (Rx)) is a translational research study aimed at transforming our understanding of cancer evolution and take a practical step towards an era of precision medicine. It employs observational cohort data and aims at analyzing intra-tumor heterogeneity in order to help the development of novel, targeted and immune based therapies. ACKNOWLEDGMENT This paper is supported by European Union's Horizon 2020 research and innovation programme under grant agreement No. 727658, project IASIS (Integration and analysis of heterogeneous big data for precision medicine and suggested treatments for different types of patients).
PY - 2019
Y1 - 2019
N2 - The vision of IASIS project is to turn the wave of big biomedical data heading our way into actionable knowledge for decision makers. This is achieved by integrating data from disparate sources, including genomics, electronic health records and bibliography, and applying advanced analytics methods to discover useful patterns. The goal is to turn large amounts of available data into actionable information to authorities for planning public health activities and policies. The integration and analysis of these heterogeneous sources of information will enable the best decisions to be made, allowing for diagnosis and treatment to be personalised to each individual. The project offers a common representation schema for the heterogeneous data sources. The iASiS infrastructure is able to convert clinical notes into usable data, combine them with genomic data, related bibliography, image data and more, and create a global knowledge base. This facilitates the use of intelligent methods in order to discover useful patterns across different resources. Using semantic integration of data gives the opportunity to generate information that is rich, auditable and reliable. This information can be used to provide better care, reduce errors and create more confidence in sharing data, thus providing more insights and opportunities. Data resources for two different disease categories are explored within the iASiS use cases, dementia and lung cancer.
AB - The vision of IASIS project is to turn the wave of big biomedical data heading our way into actionable knowledge for decision makers. This is achieved by integrating data from disparate sources, including genomics, electronic health records and bibliography, and applying advanced analytics methods to discover useful patterns. The goal is to turn large amounts of available data into actionable information to authorities for planning public health activities and policies. The integration and analysis of these heterogeneous sources of information will enable the best decisions to be made, allowing for diagnosis and treatment to be personalised to each individual. The project offers a common representation schema for the heterogeneous data sources. The iASiS infrastructure is able to convert clinical notes into usable data, combine them with genomic data, related bibliography, image data and more, and create a global knowledge base. This facilitates the use of intelligent methods in order to discover useful patterns across different resources. Using semantic integration of data gives the opportunity to generate information that is rich, auditable and reliable. This information can be used to provide better care, reduce errors and create more confidence in sharing data, thus providing more insights and opportunities. Data resources for two different disease categories are explored within the iASiS use cases, dementia and lung cancer.
KW - Big data analysis
KW - Dementia
KW - Electronic health records
KW - Genomics
KW - Lung cancer
KW - Personalized medicine
UR - http://www.scopus.com/inward/record.url?scp=85071002424&partnerID=8YFLogxK
U2 - 10.1109/CBMS.2019.00032
DO - 10.1109/CBMS.2019.00032
M3 - Conference contribution
AN - SCOPUS:85071002424
SN - 978-1-7281-2287-8
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 106
EP - 111
BT - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 June 2019 through 7 June 2019
ER -