Xiaoying Huang
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Outcomes, Evaluation Metrics, and Measurement Tools for Large Language Model Applications Research in Nursing: A Scoping Review
Conflicts of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Data Availability Statement Data sharing is not applicable to this article as no data sets were generated or analyzed during the current study.
Advancing cancer detection and treatment using longitudinal routine clinical data
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Results To build a predictive model capable of learning cancer-associated signals from routine clinical data, we developed a unified, multimodal framework, Oncoformer, and validated it on a large-scale, multi-cohort dataset (Figure 1). The core of our study is a large-scale discovery cohort, COMPASS-Main, which includes longitudinal data from 2,810,742 individuals.
By Kai Wang, Hui XU, Cheng Tang, Xian Shen, Meihao Wang, Li N Liu, Changxi Hu, Wei Wu, Zixing Zou, Bingzhou Li, Sian Liu, Jin Ho Kang, JungHo Kong, Ting Li, Xiaoying Huang, Gang Chen, Wenyang Lu, Charlotte Zhang, Yiwen Sun, Weihong Lin, Caiwen Ou, Manson Fok, Taiwa Hou, Winston Wang, Kanmin Xue, Yun Yin, Hao Yue Zhu, Jonathan S. Gootenberg, Omar O. Abudayyeh, Michael Karin, Alexandre Loupy, John Rasko, Trey Ideker, Huiyan Luo, Eric K. Oermann, Kang Zhang, Ian Ziyar
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Cell Press
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