Zobrazeno 1 - 10
of 286
pro vyhledávání: '"BENDAYAN, REBECCA"'
Autor:
Zhang, Yuezhou, Folarin, Amos A, Sun, Shaoxiong, Cummins, Nicholas, Bendayan, Rebecca, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, Laiou, Petroula, Matcham, Faith, White, Katie M, Lamers, Femke, Siddi, Sara, Simblett, Sara, Myin-Germeys, Inez, Rintala, Aki, Wykes, Til, Haro, Josep Maria, Penninx, Brenda WJH, Narayan, Vaibhav A, Hotopf, Matthew, Dobson, Richard JB
Publikováno v:
JMIR mHealth and uHealth, Vol 9, Iss 4, p e24604 (2021)
BackgroundSleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, pol
Externí odkaz:
https://doaj.org/article/59a951a328f245ffab3440386c7da5dd
Autor:
Kraljevic, Zeljko, Bean, Dan, Shek, Anthony, Bendayan, Rebecca, Hemingway, Harry, Yeung, Joshua Au, Deng, Alexander, Baston, Alfie, Ross, Jack, Idowu, Esther, Teo, James T, Dobson, Richard J
Background: Electronic Health Records hold detailed longitudinal information about each patient's health status and general clinical history, a large portion of which is stored within the unstructured text. Existing approaches focus mostly on structu
Externí odkaz:
http://arxiv.org/abs/2212.08072
Autor:
Kraljevic, Zeljko, Shek, Anthony, Bean, Daniel, Bendayan, Rebecca, Teo, James, Dobson, Richard
The data available in Electronic Health Records (EHRs) provides the opportunity to transform care, and the best way to provide better care for one patient is through learning from the data available on all other patients. Temporal modelling of a pati
Externí odkaz:
http://arxiv.org/abs/2107.03134
Autor:
Kraljevic, Zeljko, Bean, Dan, Shek, Anthony, Bendayan, Rebecca, Hemingway, Harry, Yeung, Joshua Au, Deng, Alexander, Balston, Alfred, Ross, Jack, Idowu, Esther, Teo, James T †, Dobson, Richard J B †, *
Publikováno v:
In The Lancet Digital Health April 2024 6(4):e281-e290
Autor:
Kraljevic, Zeljko, Searle, Thomas, Shek, Anthony, Roguski, Lukasz, Noor, Kawsar, Bean, Daniel, Mascio, Aurelie, Zhu, Leilei, Folarin, Amos A, Roberts, Angus, Bendayan, Rebecca, Richardson, Mark P, Stewart, Robert, Shah, Anoop D, Wong, Wai Keong, Ibrahim, Zina, Teo, James T, Dobson, Richard JB
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis. We present the open-source Medical Concept Annotation Toolkit (MedCAT) that
Externí odkaz:
http://arxiv.org/abs/2010.01165
Autor:
Mascio, Aurelie, Kraljevic, Zeljko, Bean, Daniel, Dobson, Richard, Stewart, Robert, Bendayan, Rebecca, Roberts, Angus
Text classification tasks which aim at harvesting and/or organizing information from electronic health records are pivotal to support clinical and translational research. However these present specific challenges compared to other classification task
Externí odkaz:
http://arxiv.org/abs/2005.06624
Autor:
Bendayan, Rebecca, Wu, Honghan, Kraljevic, Zeljko, Stewart, Robert, Searle, Tom, Chaturvedi, Jaya, Das-Munshi, Jayati, Ibrahim, Zina, Mascio, Aurelie, Roberts, Angus, Bean, Daniel, Dobson, Richard
Multimorbidity research in mental health services requires data from physical health conditions which is traditionally limited in mental health care electronic health records. In this study, we aimed to extract data from physical health conditions fr
Externí odkaz:
http://arxiv.org/abs/2002.08901
Autor:
Kraljevic, Zeljko, Bean, Daniel, Mascio, Aurelie, Roguski, Lukasz, Folarin, Amos, Roberts, Angus, Bendayan, Rebecca, Dobson, Richard
Biomedical documents such as Electronic Health Records (EHRs) contain a large amount of information in an unstructured format. The data in EHRs is a hugely valuable resource documenting clinical narratives and decisions, but whilst the text can be ea
Externí odkaz:
http://arxiv.org/abs/1912.10166
Akademický článek
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Publikováno v:
EMNLP/IJCNLP 2019
We present MedCATTrainer an interface for building, improving and customising a given Named Entity Recognition and Linking (NER+L) model for biomedical domain text. NER+L is often used as a first step in deriving value from clinical text. Collecting
Externí odkaz:
http://arxiv.org/abs/1907.07322