Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Naima Oubenali"'
Autor:
Mohamed El Azzouzi, Gouenou Coatrieux, Reda Bellafqira, Denis Delamarre, Christine Riou, Naima Oubenali, Sandie Cabon, Marc Cuggia, Guillaume Bouzillé
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-18 (2024)
Abstract Background Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to pr
Externí odkaz:
https://doaj.org/article/e93c4f82eaa84b88bf5ffd1185ee8736
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 22, Iss 1, Pp 1-14 (2022)
Abstract Background Analyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural la
Externí odkaz:
https://doaj.org/article/8c46e1f6e4c44ada85ac9d5a9a10a0f5
Publikováno v:
Studies in Health Technology and Informatics
Studies in Health Technology and Informatics, 2022, 298, pp.51-55. ⟨10.3233/SHTI220906⟩
Digital Professionalism in Health and Care: Developing the Workforce, Building the Future
Digital Professionalism in Health and Care: Developing the Workforce, Building the Future, IOS Press, 2022, Studies in Health Technology and Informatics
Studies in Health Technology and Informatics, 2022, 298, pp.51-55. ⟨10.3233/SHTI220906⟩
Digital Professionalism in Health and Care: Developing the Workforce, Building the Future
Digital Professionalism in Health and Care: Developing the Workforce, Building the Future, IOS Press, 2022, Studies in Health Technology and Informatics
International audience; Health data science is an emerging discipline that bridges computer science, statistics and health domain knowledge. This consists of taking advantage of the large volume of data, often complex, to extract information to impro
Publikováno v:
BMC medical informatics and decision making. 22(1)
BackgroundAnalyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language pro