Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Aleksandar, Savkov"'
Autor:
Francesco, Moramarco, Damir, Juric, Aleksandar, Savkov, Jack, Flann, Maria, Lehl, Kristian, Boda, Tessa, Grafen, Vitalii, Zhelezniak, Sunir, Gohil, Alex Papadopoulos, Korfiatis, Nils, Hammerla
Publikováno v:
AMIA Annu Symp Proc
Clinical notes are an efficient way to record patient information but are notoriously hard to decipher for non-experts. Automatically simplifying medical text can empower patients with valuable information about their health, while saving clinicians
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::499193d8b979732d94f205beb2475d32
https://pubmed.ncbi.nlm.nih.gov/35308976
https://pubmed.ncbi.nlm.nih.gov/35308976
Autor:
Francesco Moramarco, Alex Papadopoulos Korfiatis, Mark Perera, Damir Juric, Jack Flann, Ehud Reiter, Anya Belz, Aleksandar Savkov
Publikováno v:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
In recent years, machine learning models have rapidly become better at generating clinical consultation notes; yet, there is little work on how to properly evaluate the generated consultation notes to understand the impact they may have on both the c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c87f31e7209613cab6731f9efbb50df
https://doi.org/10.18653/v1/2022.acl-long.394
https://doi.org/10.18653/v1/2022.acl-long.394
Publikováno v:
University of Aberdeen-PURE
We propose a method for evaluating the quality of generated text by asking evaluators to count facts, and computing precision, recall, f-score, and accuracy from the raw counts. We believe this approach leads to a more objective and easier to reprodu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9fcc1751d11e97de795ebc7ebb69cc61
http://arxiv.org/abs/2104.04412
http://arxiv.org/abs/2104.04412
Publikováno v:
ACL
Word embedding-based similarity measures are currently among the top-performing methods on unsupervised semantic textual similarity (STS) tasks. Recent work has increasingly adopted a statistical view on these embeddings, with some of the top approac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b8c53422e1b2373f7b9095dc6fb086fb
https://doi.org/10.18653/v1/2020.acl-main.741
https://doi.org/10.18653/v1/2020.acl-main.741
Publikováno v:
EMNLP/IJCNLP (1)
Similarity measures based purely on word embeddings are comfortably competing with much more sophisticated deep learning and expert-engineered systems on unsupervised semantic textual similarity (STS) tasks. In contrast to commonly used geometric app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8196c927878a7a044de32e679a635fd8
http://arxiv.org/abs/1910.02902
http://arxiv.org/abs/1910.02902
Publikováno v:
NAACL-HLT (1)
A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals and many clever tricks. By contrast, little attention has been devoted
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8dcb1f7264476e83aeff8c6f23ae2dd6
Publikováno v:
13th Workshop on Biomedical Natural Language Processing (BioNLP)
BioNLP@ACL
BioNLP@ACL
Free text notes typed by primary care physicians during patient consultations typically contain highly non-canonical language. Shallow syntactic analysis of free text notes can help to reveal valuable information for the study of disease and treatmen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bcdf9cbc850743c6d79f4967d6543c3
http://sro.sussex.ac.uk/id/eprint/53736/1/W14-3411.pdf
http://sro.sussex.ac.uk/id/eprint/53736/1/W14-3411.pdf
Publikováno v:
Language Resources and Evaluation
The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraph
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e4cded1bf929e22eff6eb83f31089567
Publikováno v:
University of Aberdeen-PURE
Automatic summarisation has the potential to aid physicians in streamlining clerical tasks such as note taking. But it is notoriously difficult to evaluate these systems and demonstrate that they are safe to be used in a clinical setting. To circumve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f73f461e9e5cd5d9c1f5f814e3f2628
https://abdn.pure.elsevier.com/en/en/researchoutput/a-preliminary-study-on-evaluating-consultation-notes-with-postediting(d9fd9d06-32cf-4190-a800-d22b077b9843).html
https://abdn.pure.elsevier.com/en/en/researchoutput/a-preliminary-study-on-evaluating-consultation-notes-with-postediting(d9fd9d06-32cf-4190-a800-d22b077b9843).html