Zobrazeno 1 - 10
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pro vyhledávání: '"May, D"'
Polygenic risk scores (PRSs) can significantly enhance breast cancer risk prediction when combined with clinical risk factor data. While many studies have explored the value-add of PRSs, little is known about the potential impact of gene-by-gene or g
Externí odkaz:
http://arxiv.org/abs/2407.20978
Autor:
Shi, Wenqi, Xu, Ran, Zhuang, Yuchen, Yu, Yue, Sun, Haotian, Wu, Hang, Yang, Carl, Wang, May D.
Despite their improved capabilities in generation and reasoning, adapting large language models (LLMs) to the biomedical domain remains challenging due to their immense size and corporate privacy. In this work, we propose MedAdapter, a unified post-h
Externí odkaz:
http://arxiv.org/abs/2405.03000
Autor:
Xu, Ran, Shi, Wenqi, Yu, Yue, Zhuang, Yuchen, Zhu, Yanqiao, Wang, May D., Ho, Joyce C., Zhang, Chao, Yang, Carl
Publikováno v:
EMNLP 2024
Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient publicly annotated biomedical data and computational resources. We present B
Externí odkaz:
http://arxiv.org/abs/2404.18443
Autor:
Xu, Ran, Shi, Wenqi, Yu, Yue, Zhuang, Yuchen, Jin, Bowen, Wang, May D., Ho, Joyce C., Yang, Carl
Publikováno v:
ACL 2024
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources, converts them into text format, and uses dense retrieval to obtain informati
Externí odkaz:
http://arxiv.org/abs/2403.00815
Autor:
Shi, Wenqi, Xu, Ran, Zhuang, Yuchen, Yu, Yue, Zhang, Jieyu, Wu, Hang, Zhu, Yuanda, Ho, Joyce, Yang, Carl, Wang, May D.
Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving. We propose EHRAgent, an LLM agent empowered with a code interface,
Externí odkaz:
http://arxiv.org/abs/2401.07128
We study in detail the inner 600 pc of the Seyfert 2 galaxy ESO138-G001 by means of the SOAR Integral Field Spectrograph (SIFS) attached to the SOAR telescope. This source is known for displaying a very rich coronal line spectrum and a blob of high-e
Externí odkaz:
http://arxiv.org/abs/2312.09184
In the context of surgery, robots can provide substantial assistance by performing small, repetitive tasks such as suturing, needle exchange, and tissue retraction, thereby enabling surgeons to concentrate on more complex aspects of the procedure. Ho
Externí odkaz:
http://arxiv.org/abs/2309.00837
Autor:
Monica Isgut, Felipe Giuste, Logan Gloster, Aniketh Swain, Katherine Choi, Andrew Hornback, Shriprasad R. Deshpande, May D. Wang
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Polygenic risk scores (PRSs) hold promise in their potential translation into clinical settings to improve disease risk prediction. An important consideration in integrating PRSs into clinical settings is to gain an understanding of how to i
Externí odkaz:
https://doaj.org/article/f4bfbc2d69ef4750b425fbca41c7a9eb
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract Background Learning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating and optimizing these p
Externí odkaz:
https://doaj.org/article/6817526e924b44cea5f537f7c1e1ab2f
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract Background Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only pre
Externí odkaz:
https://doaj.org/article/f90c78138b4e4ccb83187efd81455c80