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pro vyhledávání: '"Ji, Yuelyu"'
Large language models (LLMs) have exhibited complex reasoning abilities by generating question rationales and demonstrated exceptional performance in natural language processing (NLP) tasks. However, these reasoning capabilities generally emerge in m
Externí odkaz:
http://arxiv.org/abs/2410.03663
Knowledge sharing is crucial in healthcare, especially when leveraging data from multiple clinical sites to address data scarcity, reduce costs, and enable timely interventions. Transfer learning can facilitate cross-site knowledge transfer, but a ma
Externí odkaz:
http://arxiv.org/abs/2409.13893
Autor:
Ji, Yuelyu, Li, Zhuochun, Meng, Rui, Sivarajkumar, Sonish, Wang, Yanshan, Yu, Zeshui, Ji, Hui, Han, Yushui, Zeng, Hanyu, He, Daqing
This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research understandable to laymen through advanced Natural Language Processing (NLP) techniques. Our Retrieval Augmented Generation (RAG) solution, enhanced by a
Externí odkaz:
http://arxiv.org/abs/2405.13179
In this study, we aim to address the task of assertion detection when extracting medical concepts from clinical notes, a key process in clinical natural language processing (NLP). Assertion detection in clinical NLP usually involves identifying asser
Externí odkaz:
http://arxiv.org/abs/2401.17602
Text generation in image-based platforms, particularly for music-related content, requires precise control over text styles and the incorporation of emotional expression. However, existing approaches often need help to control the proportion of exter
Externí odkaz:
http://arxiv.org/abs/2310.01248
The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potenti
Externí odkaz:
http://arxiv.org/abs/2306.17257
Akademický článek
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Autor:
Ji Y; Department of Information Science, School of Computing and Information, University of Pittsburgh, Pittsburgh,USA., Gao Y; Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, USA., Bao R; Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, USA., Li Q; School of Business, State University of New York at New Paltz, New Paltz, USA., Liu D; Department of Information Science, School of Computing and Information, University of Pittsburgh Pittsburgh, USA., Sun Y; Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh Pittsburgh, USA., Ye Y; Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, USA.
Publikováno v:
IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics [IEEE Int Conf Healthc Inform] 2023 Jun; Vol. 2023, pp. 138-144. Date of Electronic Publication: 2023 Dec 11.