Zobrazeno 1 - 10
of 533
pro vyhledávání: '"Concept extraction"'
Autor:
Anton Agafonov, Andrew Ponomarev
Publikováno v:
Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 34, Iss 1, Pp 9-https://youtu.be/dSfBBvMbDA0 (2023)
The need for AI explainability, which involves helping humans understand why an AI algorithm arrived at a particular decision, is crucial in numerous critical applications. Although deep neural networks play a significant role in modern AI, they inhe
Externí odkaz:
https://doaj.org/article/72d9f0219dfd4253862143bf37df1f2d
Publikováno v:
Frontiers in Digital Health, Vol 5 (2023)
The extraction of patient signs and symptoms recorded as free text in electronic health records is critical for precision medicine. Once extracted, signs and symptoms can be made computable by mapping to signs and symptoms in an ontology. Extracting
Externí odkaz:
https://doaj.org/article/94bc0b37db8348daa155479357f21541
Autor:
Alexandra K Marr, Davide Bedognetti, Basirudeen Syed Ahamed Kabeer, Fatima Al Ali, Nico Marr, Jessica Roelands, Mathieu Garand, Zohreh Tatari-Calderone, Mohammed Toufiq, Darawan Rinchai, Damien Chaussabel, Mohamed Alfaki
Publikováno v:
F1000Research, Vol 10 (2023)
Early-career researchers must acquire the skills necessary to effectively search and extract information from biomedical literature. This ability is for instance crucial for evaluating the novelty of experimental results, and assessing potential publ
Externí odkaz:
https://doaj.org/article/201ccb635b5943e49dcfceab2f464d0c
WERECE: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement
Autor:
Jingxiu Huang, Ruofei Ding, Xiaomin Wu, Shumin Chen, Jiale Zhang, Lixiang Liu, Yunxiang Zheng
Publikováno v:
Applied Sciences, Vol 13, Iss 22, p 12307 (2023)
The era of educational big data has sparked growing interest in extracting and organizing educational concepts from massive amounts of information. Outcomes are of the utmost importance for artificial intelligence–empowered teaching and learning. U
Externí odkaz:
https://doaj.org/article/acb53d28a264496ab1b46c25d4912181
Autor:
Gabrielle Stinton, Jane A. Lieviant, Sylvia Kam, Jiin Ying Lim, Jasmine Chew-Yin Goh, Weng Khong Lim, Gareth Baynam, Tele Tan, Duc-Son Pham, Saumya Shekhar Jamuar
Publikováno v:
Rare, Vol 1, Iss , Pp 100007- (2023)
Leveraging Artificial Intelligence (AI) within the rare disease diagnostic odyssey can facilitate a decrease in diagnostic times and an increase in diagnostic rates. Among the steps involved in the odyssey, this project focused on utilizing AI to aut
Externí odkaz:
https://doaj.org/article/322f328574b045dab9429b0b8a61fc93
Enhanced neurologic concept recognition using a named entity recognition model based on transformers
Publikováno v:
Frontiers in Digital Health, Vol 4 (2022)
Although deep learning has been applied to the recognition of diseases and drugs in electronic health records and the biomedical literature, relatively little study has been devoted to the utility of deep learning for the recognition of signs and sym
Externí odkaz:
https://doaj.org/article/9b27d25f6c334a57917fb41a7a33f61c
Akademický článek
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Akademický článek
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Akademický článek
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Publikováno v:
IEEE Access, Vol 9, Pp 118736-118756 (2021)
Sentiment could be expressed implicitly or explicitly in the text. Hence, it is the main challenge for current sentiment analysis (SA) approaches to identify hidden sentiments, other common challenges include false classification of opinion words, ig
Externí odkaz:
https://doaj.org/article/3d9dfac577c94c25b1c54c15946f3a70