Zobrazeno 1 - 10
of 2 973
pro vyhledávání: '"Hanbury A"'
Autor:
Smith AB, Hanbury A, Whitty JA, Beitia Ortiz de Zarate I, Hammes F, de Pouvourville G, Buesch K
Publikováno v:
Patient Related Outcome Measures, Vol Volume 13, Pp 21-30 (2022)
Adam B Smith,1 Andria Hanbury,1 Jennifer A Whitty,2 Igor Beitia Ortiz de Zarate,3 Florence Hammes,3 Gérard de Pouvourville,4 Katharina Buesch5 1York Health Economics Consortium, University of York, York, UK; 2Norwich Medical School, University of Ea
Externí odkaz:
https://doaj.org/article/380087cb7b5f4894bc902a4442c0e726
Publikováno v:
Patient Related Outcome Measures, Vol Volume 12, Pp 237-246 (2021)
Adam B Smith,1 Andria Hanbury,1 Igor Beitia Ortiz de Zarate,2 Florence Hammes,2 Gerard de Pouvourville,3 Katharina Buesch4 1York Health Economics Consortium, University of York, York, UK; 2PTC Therapeutics France, Paris, France; 3Department of Econom
Externí odkaz:
https://doaj.org/article/97b81241a445465db8fe5227436e1114
Publikováno v:
Patient Related Outcome Measures, Vol Volume 12, Pp 97-106 (2021)
Adam B Smith,1 Andria Hanbury,1 Jennifer A Whitty,2 Katharina Buesch3 1York Health Economics Consortium, University of York, York, UK; 2Norwich Medical School, University of East Anglia, Norwich, UK; 3PTC Therapeutics, Zug, 6300, SwitzerlandCorrespon
Externí odkaz:
https://doaj.org/article/ced75af54eb94a72bfc2425ec5f5bea7
Publikováno v:
Patient Related Outcome Measures, Vol Volume 12, Pp 1-12 (2021)
Andria Hanbury,1 Adam B Smith,1 Katharina Buesch2 1York Health Economics Consortium, University of York, York YO10 5NQ, UK; 2PTC Therapeutics, Zug 6300, SwitzerlandCorrespondence: Adam B SmithYork Health Economics Consortium, Enterprise House, Innova
Externí odkaz:
https://doaj.org/article/f506a2f10e1847d89c6af02da4332336
Autor:
Pachinger, Pia, Goldzycher, Janis, Planitzer, Anna Maria, Kusa, Wojciech, Hanbury, Allan, Neidhardt, Julia
Model interpretability in toxicity detection greatly profits from token-level annotations. However, currently such annotations are only available in English. We introduce a dataset annotated for offensive language detection sourced from a news forum,
Externí odkaz:
http://arxiv.org/abs/2406.08080
Systematic literature reviews (SLRs) play an essential role in summarising, synthesising and validating scientific evidence. In recent years, there has been a growing interest in using machine learning techniques to automate the identification of rel
Externí odkaz:
http://arxiv.org/abs/2311.12474
Search methods based on Pretrained Language Models (PLM) have demonstrated great effectiveness gains compared to statistical and early neural ranking models. However, fine-tuning PLM-based rankers requires a great amount of annotated training data. A
Externí odkaz:
http://arxiv.org/abs/2309.06131
Keeping up with research and finding related work is still a time-consuming task for academics. Researchers sift through thousands of studies to identify a few relevant ones. Automation techniques can help by increasing the efficiency and effectivene
Externí odkaz:
http://arxiv.org/abs/2309.01684
Clinical trials (CTs) often fail due to inadequate patient recruitment. This paper tackles the challenges of CT retrieval by presenting an approach that addresses the patient-to-trials paradigm. Our approach involves two key components in a pipeline-
Externí odkaz:
http://arxiv.org/abs/2307.00381
Current methods of evaluating search strategies and automated citation screening for systematic literature reviews typically rely on counting the number of relevant and not relevant publications. This established practice, however, does not accuratel
Externí odkaz:
http://arxiv.org/abs/2306.17614