Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Julia Ive"'
Publikováno v:
Frontiers in Digital Health, Vol 5 (2023)
Two significant obstacles exist preventing the widespread usage of Deep Learning (DL) models for predicting healthcare outcomes in general and mental health conditions in particular. Firstly, DL models do not quantify the uncertainty in their predict
Externí odkaz:
https://doaj.org/article/474c2bccbefa45ffa501ef16d7ae84de
Autor:
Julia Ive
Publikováno v:
Frontiers in Digital Health, Vol 4 (2022)
In today’s world it seems fair to say that extensive digital data sharing is the price we pay for the technological advances we have seen achieved as a result of AI systems analysing large quantities of data in a relatively short time. Where such A
Externí odkaz:
https://doaj.org/article/f6c6498262ec42d48a7c753919d8decb
Autor:
Julia Ive
Publikováno v:
Computational Linguistics, Vol 48, Iss 1, Pp 233-235 (2022)
Externí odkaz:
https://doaj.org/article/5658af5926b94913883ea0b3fcfc3206
Autor:
Jingqing Zhang, Luis Daniel Bolanos Trujillo, Ashwani Tanwar, Julia Ive, Vibhor Gupta, Yike Guo
Publikováno v:
BMJ Health & Care Informatics, Vol 29, Iss 1 (2022)
Objective Clinical notes contain information that has not been documented elsewhere, including responses to treatment and clinical findings, which are crucial for predicting key outcomes in patients in acute care. In this study, we propose the automa
Externí odkaz:
https://doaj.org/article/5320a728a2e54424b17326cf9f44357e
Autor:
Julia Ive, Natalia Viani, Joyce Kam, Lucia Yin, Somain Verma, Stephen Puntis, Rudolf N. Cardinal, Angus Roberts, Robert Stewart, Sumithra Velupillai
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-9 (2020)
Abstract A serious obstacle to the development of Natural Language Processing (NLP) methods in the clinical domain is the accessibility of textual data. The mental health domain is particularly challenging, partly because clinical documentation relie
Externí odkaz:
https://doaj.org/article/70bae84263554a11a15139b29889876f
Publikováno v:
Exp Biol Med (Maywood)
Phenotypic information of patients, as expressed in clinical text, is important in many clinical applications such as identifying patients at risk of hard-to-diagnose conditions. Extracting and inferring some phenotypes from clinical text requires nu
Publikováno v:
Multimodal AI in Healthcare ISBN: 9783031147708
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0aa83795454fb0c7886fa5a6fed7acea
https://doi.org/10.1007/978-3-031-14771-5_2
https://doi.org/10.1007/978-3-031-14771-5_2
Autor:
Ruiqing Zhang, Chuanqiang Zhang, Zhongjun He, Hua Wu, Haifeng Wang, Liang Huang, Qun Liu, Julia Ive, Wolfgang Macherey
Publikováno v:
Proceedings of the Third Workshop on Automatic Simultaneous Translation.
Autor:
Atijit Anuchitanukul, Julia Ive
Publikováno v:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
Publikováno v:
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology.