Zobrazeno 1 - 10
of 16 430
pro vyhledávání: '"synthetic data"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Abstract This study addresses challenges related to privacy issues in utilizing medical data, particularly the protection of personal information. To overcome this obstacle, the research focuses on data synthesis using real-world time-series generati
Externí odkaz:
https://doaj.org/article/8f43a7f4e2b041709f200ec4820c628c
Publikováno v:
BMC Medical Research Methodology, Vol 24, Iss 1, Pp 1-14 (2024)
Abstract Background Synthetic Electronic Health Records (EHRs) are becoming increasingly popular as a privacy enhancing technology. However, for longitudinal EHRs specifically, little research has been done into how to properly evaluate synthetically
Externí odkaz:
https://doaj.org/article/3a8b76e7aafb44debb6b26488488c5bb
Publikováno v:
Orphanet Journal of Rare Diseases, Vol 19, Iss 1, Pp 1-8 (2024)
Abstract Background Globally, researchers are working on projects aiming to enhance the availability of data for rare disease research. While data sharing remains critical, developing suitable methods is challenging due to the specific sensitivity an
Externí odkaz:
https://doaj.org/article/fa589ac1d392488da873d187ea3f33f4
Autor:
Vasileios C. Pezoulas, Dimitrios I. Zaridis, Eugenia Mylona, Christos Androutsos, Kosmas Apostolidis, Nikolaos S. Tachos, Dimitrios I. Fotiadis
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 2892-2910 (2024)
Synthetic data generation has emerged as a promising solution to overcome the challenges which are posed by data scarcity and privacy concerns, as well as, to address the need for training artificial intelligence (AI) algorithms on unbiased data with
Externí odkaz:
https://doaj.org/article/1595553cc13b4b048a4661b05bb4ccd8
Publikováno v:
SICE Journal of Control, Measurement, and System Integration, Vol 17, Iss 1, Pp 87-105 (2024)
Efficiently recognizing Activities of Daily Living (ADLs) requires overcoming challenges in collecting datasets through innovative approaches. Simultaneously, it involves adapting to the demand for interpreting human activities amidst temporal sequen
Externí odkaz:
https://doaj.org/article/268b97df65cf448bb69bd6e75c4befcd
Autor:
Nicolas Alexander Schulz, Jasmin Carus, Alexander Johannes Wiederhold, Ole Johanns, Frederik Peters, Natalie Rath, Katharina Rausch, Bernd Holleczek, Alexander Katalinic, the AI-CARE Working Group, Christopher Gundler
Publikováno v:
BMC Medical Research Methodology, Vol 24, Iss 1, Pp 1-13 (2024)
Abstract Background Generating synthetic patient data is crucial for medical research, but common approaches build up on black-box models which do not allow for expert verification or intervention. We propose a highly available method which enables s
Externí odkaz:
https://doaj.org/article/dec1b1d848d74098abd187748b4ee6cc
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-14 (2024)
Abstract Background Consider a setting where multiple parties holding sensitive data aim to collaboratively learn population level statistics, but pooling the sensitive data sets is not possible due to privacy concerns and parties are unable to engag
Externí odkaz:
https://doaj.org/article/e8b58c63e214472cb570a208b150005f
Publikováno v:
AI, Vol 5, Iss 2, Pp 576-593 (2024)
In this study, we developed and explored a methodical image augmentation technique for swimmer localisation in northern German outdoor lake environments. When it comes to enhancing swimmer safety, a main issue we have to deal with is the lack of real
Externí odkaz:
https://doaj.org/article/b1349c4348d04c57844a589972d50888
Publikováno v:
AI, Vol 5, Iss 2, Pp 667-685 (2024)
Generating synthetic data is a complex task that necessitates accurately replicating the statistical and mathematical properties of the original data elements. In sectors such as finance, utilizing and disseminating real data for research or model de
Externí odkaz:
https://doaj.org/article/8c2533a4dd064323b53c6efb0e002e12
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-56 (2024)
Abstract Synthetic data generation describes the process of learning the underlying distribution of a given real dataset in a model, which is, in turn, sampled to produce new data objects still adhering to the original distribution. This approach oft
Externí odkaz:
https://doaj.org/article/de615751470e4fbb886d39f9359045a0