Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Alison Q. O'Neil"'
Autor:
Arlene Casey, Emma Davidson, Claire Grover, Richard Tobin, Andreas Grivas, Huayu Zhang, Patrick Schrempf, Alison Q. O’Neil, Liam Lee, Michael Walsh, Freya Pellie, Karen Ferguson, Vera Cvoro, Honghan Wu, Heather Whalley, Grant Mair, William Whiteley, Beatrice Alex
Publikováno v:
Frontiers in Digital Health, Vol 5 (2023)
BackgroundNatural language processing (NLP) has the potential to automate the reading of radiology reports, but there is a need to demonstrate that NLP methods are adaptable and reliable for use in real-world clinical applications.MethodsWe tested th
Externí odkaz:
https://doaj.org/article/0c251b629e384478bfd162bf6dd1e303
Autor:
Pedro Sanchez, Jeremy P. Voisey, Tian Xia, Hannah I. Watson, Alison Q. O’Neil, Sotirios A. Tsaftaris
Publikováno v:
Royal Society Open Science, Vol 9, Iss 8 (2022)
Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an int
Externí odkaz:
https://doaj.org/article/08840578e8bc41c4a1d5f53a69146b81
Autor:
Patrick Schrempf, Hannah Watson, Eunsoo Park, Maciej Pajak, Hamish MacKinnon, Keith W. Muir, David Harris-Birtill, Alison Q. O’Neil
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 3, Iss 2, Pp 299-317 (2021)
Training medical image analysis models traditionally requires large amounts of expertly annotated imaging data which is time-consuming and expensive to obtain. One solution is to automatically extract scan-level labels from radiology reports. Previou
Externí odkaz:
https://doaj.org/article/3bf3f00c3bb94930aaf4518364a33889
Autor:
Marija Jegorova, Chaitanya Kaul, Charlie Mayor, Alison Q. O'Neil, Alexander Weir, Roderick Murray-Smith, Sotirios A. Tsaftaris
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. :1-20
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data. We
Publikováno v:
Medical Applications with Disentanglements ISBN: 9783031250453
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f8da425c8f0fe1247d06750cbe049633
https://doi.org/10.1007/978-3-031-25046-0_2
https://doi.org/10.1007/978-3-031-25046-0_2
Publikováno v:
2022, ' Evaluating the Impact on Clinical Task Efficiency of a Natural Language Processing Algorithm for Searching Medical Documents : Prospective Crossover Study ', JMIR Medical Informatics, vol. 10, no. 10, pp. e39616 . https://doi.org/10.2196/39616
Background Information retrieval (IR) from the free text within electronic health records (EHRs) is time consuming and complex. We hypothesize that natural language processing (NLP)–enhanced search functionality for EHRs can make clinical workflows
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53fb1708fb71e295e7672db191353709
https://www.pure.ed.ac.uk/ws/files/306061214/Evaluating_the_Impact_on_Clinical_Task_Efficiency_of_a_Natural_Language_Processing_Algorithm_for_Searching_Medical_Documents.pdf
https://www.pure.ed.ac.uk/ws/files/306061214/Evaluating_the_Impact_on_Clinical_Task_Efficiency_of_a_Natural_Language_Processing_Algorithm_for_Searching_Medical_Documents.pdf
BackgroundInformation retrieval (IR) from the free text within Electronic Health Records (EHRs) is time-consuming and complex. We hypothesise that Natural Language Processing (NLP)-enhanced search functionality for EHRs can make clinical workflows mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b85c8485a38d8031af8cda4c740da940
https://doi.org/10.1101/2022.05.24.22275490
https://doi.org/10.1101/2022.05.24.22275490
Autor:
David Zimmerer, Peter M. Full, Fabian Isensee, Paul Jager, Tim Adler, Jens Petersen, Gregor Kohler, Tobias Ross, Annika Reinke, Antanas Kascenas, Bjorn Sand Jensen, Alison Q. O'Neil, Jeremy Tan, Benjamin Hou, James Batten, Huaqi Qiu, Bernhard Kainz, Nina Shvetsova, Irina Fedulova, Dmitry V. Dylov, Baolun Yu, Jianyang Zhai, Jingtao Hu, Runxuan Si, Sihang Zhou, Siqi Wang, Xinyang Li, Xuerun Chen, Yang Zhao, Sergio Naval Marimont, Giacomo Tarroni, Victor Saase, Lena Maier-Hein, Klaus Maier-Hein
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they oft
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bd09f323a99e76832914cf9e01598d2
http://hdl.handle.net/10044/1/96881
http://hdl.handle.net/10044/1/96881
Publikováno v:
Jacenków, G, O'Neil, A & Tsaftaris, S A 2022, Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers . in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) . International Symposium on Biomedical Imaging Proceedings, vol. 2022, IEEE International Symposium on Biomedical Imaging (ISBI) 2022, Kolkata, 28/03/22 . https://doi.org/10.1109/ISBI52829.2022.9761567
When a clinician refers a patient for an imaging exam, they include the reason (e.g. relevant patient history, suspected disease) in the scan request; this appears as the indication field in the radiology report. The interpretation and reporting of t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed69b586e4ebde50ec916c91ca4eac13
http://arxiv.org/abs/2202.06076
http://arxiv.org/abs/2202.06076
Autor:
Tran Quoc Bao Tran, Stefanie Lip, Clea du Toit, Tejas Kumar Kalaria, Ravi K. Bhaskar, Alison Q. O’Neil, Beata Graff, Michał Hoffmann, Anna Szyndler, Katarzyna Polonis, Jacek Wolf, Sandeep Reddy, Krzysztof Narkiewicz, Indranil Dasgupta, Anna F. Dominiczak, Shyam Visweswaran, Linsay McCallum, Sandosh Padmanabhan
Publikováno v:
SSRN Electronic Journal.