Privacy-preserving Systems Medicine
Конференцијски прилог (Објављена верзија)
,
© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
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Приказ свих података о документуАпстракт
Artificial intelligence (AI) offers game-changing opportunities to healthcare. However, it
also harbors risks to patient privacy in particular when dealing with sensitive clinical data
stored in critical healthcare IT infrastructure. Specifically, data exchange over the internet
is perceived insurmountable, posing a roadblock hampering big-data-based medical
innovations.
We created a novel AI platform, the FeatureCloud AI app store that is based on the idea of
federated learning where only model parameters are communicated. To maximize privacy,
sensitive datasets remain stored locally and are analysed behind safe firewalls to assure
the high standards in data privacy in order to (by design) comply with the strict GDPR.
We will exemplarly investigate the power of FeatureCloud apps for decentralized (1)
genome-wide association studies (GWAS), (2) gene expression data mining, and (3) timeto-
event data analytics to demonstrate how FeatureCloud may enhance worldwide
collaborati...on, accelerate innovation, and democratize scientific data usage. We show
that apps developed in FeatureCloud can produce highly similar results compared to
centralized approaches and scale well for an increasing number of participating sites.
FeatureCloud is a no-code platform for federated learning apps having the potential to
vastly increase the accessibility of privacy-preserving and distributed data analysis in
biomedicine and beyond.
Кључне речи:
bioinformatics / data mining / federated learningИзвор:
4th Belgrade Bioinformatics Conference, 2023, 4, 8-8Издавач:
- Belgrade : Institute of molecular genetics and genetic engineering
Финансирање / пројекти:
- This project has received funding from the European Union’s Horizon2020 research and innovation programme under grant agreement No 826078
Напомена:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
Колекције
Институција/група
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Baumbach, Jan PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/1943 AB - Artificial intelligence (AI) offers game-changing opportunities to healthcare. However, it also harbors risks to patient privacy in particular when dealing with sensitive clinical data stored in critical healthcare IT infrastructure. Specifically, data exchange over the internet is perceived insurmountable, posing a roadblock hampering big-data-based medical innovations. We created a novel AI platform, the FeatureCloud AI app store that is based on the idea of federated learning where only model parameters are communicated. To maximize privacy, sensitive datasets remain stored locally and are analysed behind safe firewalls to assure the high standards in data privacy in order to (by design) comply with the strict GDPR. We will exemplarly investigate the power of FeatureCloud apps for decentralized (1) genome-wide association studies (GWAS), (2) gene expression data mining, and (3) timeto- event data analytics to demonstrate how FeatureCloud may enhance worldwide collaboration, accelerate innovation, and democratize scientific data usage. We show that apps developed in FeatureCloud can produce highly similar results compared to centralized approaches and scale well for an increasing number of participating sites. FeatureCloud is a no-code platform for federated learning apps having the potential to vastly increase the accessibility of privacy-preserving and distributed data analysis in biomedicine and beyond. PB - Belgrade : Institute of molecular genetics and genetic engineering C3 - 4th Belgrade Bioinformatics Conference T1 - Privacy-preserving Systems Medicine EP - 8 SP - 8 VL - 4 UR - https://hdl.handle.net/21.15107/rcub_imagine_1943 ER -
@conference{ author = "Baumbach, Jan", year = "2023", abstract = "Artificial intelligence (AI) offers game-changing opportunities to healthcare. However, it also harbors risks to patient privacy in particular when dealing with sensitive clinical data stored in critical healthcare IT infrastructure. Specifically, data exchange over the internet is perceived insurmountable, posing a roadblock hampering big-data-based medical innovations. We created a novel AI platform, the FeatureCloud AI app store that is based on the idea of federated learning where only model parameters are communicated. To maximize privacy, sensitive datasets remain stored locally and are analysed behind safe firewalls to assure the high standards in data privacy in order to (by design) comply with the strict GDPR. We will exemplarly investigate the power of FeatureCloud apps for decentralized (1) genome-wide association studies (GWAS), (2) gene expression data mining, and (3) timeto- event data analytics to demonstrate how FeatureCloud may enhance worldwide collaboration, accelerate innovation, and democratize scientific data usage. We show that apps developed in FeatureCloud can produce highly similar results compared to centralized approaches and scale well for an increasing number of participating sites. FeatureCloud is a no-code platform for federated learning apps having the potential to vastly increase the accessibility of privacy-preserving and distributed data analysis in biomedicine and beyond.", publisher = "Belgrade : Institute of molecular genetics and genetic engineering", journal = "4th Belgrade Bioinformatics Conference", title = "Privacy-preserving Systems Medicine", pages = "8-8", volume = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_1943" }
Baumbach, J.. (2023). Privacy-preserving Systems Medicine. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic engineering., 4, 8-8. https://hdl.handle.net/21.15107/rcub_imagine_1943
Baumbach J. Privacy-preserving Systems Medicine. in 4th Belgrade Bioinformatics Conference. 2023;4:8-8. https://hdl.handle.net/21.15107/rcub_imagine_1943 .
Baumbach, Jan, "Privacy-preserving Systems Medicine" in 4th Belgrade Bioinformatics Conference, 4 (2023):8-8, https://hdl.handle.net/21.15107/rcub_imagine_1943 .