Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Kraljevic, Zeljko"'
Autor:
Kraljevic, Zeljko, Shek, Anthony, Yeung, Joshua Au, Sheldon, Ewart Jonathan, Al-Agil, Mohammad, Shuaib, Haris, Bai, Xi, Noor, Kawsar, Shah, Anoop D., Dobson, Richard, Teo, James
Protecting patient privacy in healthcare records is a top priority, and redaction is a commonly used method for obscuring directly identifiable information in text. Rule-based methods have been widely used, but their precision is often low causing ov
Externí odkaz:
http://arxiv.org/abs/2310.04468
Autor:
Torbarina, Lovre, Ferkovic, Tin, Roguski, Lukasz, Mihelcic, Velimir, Sarlija, Bruno, Kraljevic, Zeljko
The increasing adoption of natural language processing (NLP) models across industries has led to practitioners' need for machine learning systems to handle these models efficiently, from training to serving them in production. However, training, depl
Externí odkaz:
http://arxiv.org/abs/2308.08234
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:
Torbarina, Lovre, Ferkovic, Tin, Roguski, Lukasz, Mihelcic, Velimir, Sarlija, Bruno, Kraljevic, Zeljko
Publikováno v:
In Natural Language Processing Journal June 2024 7
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:
Ibrahim, Zina M, Bean, Daniel, Searle, Thomas, Wu, Honghan, Shek, Anthony, Kraljevic, Zeljko, Galloway, James, Norton, Sam, Teo, James T, Dobson, Richard JB
The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes.
Externí odkaz:
http://arxiv.org/abs/2011.09361
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