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Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance.

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第一作者: Xiang,Pan
第一单位: Department of Biomedical Engineering, Worcester Polytechnic Institute, MA 01609, USA; Vascular Biology Program, Boston Children's Hospital, Boston, MA 02115, USA.
作者单位: Department of Biomedical Engineering, Worcester Polytechnic Institute, MA 01609, USA; Vascular Biology Program, Boston Children's Hospital, Boston, MA 02115, USA. [1] Department of Biomedical Engineering, Worcester Polytechnic Institute, MA 01609, USA. [2] Robotics Engineering Program, Worcester Polytechnic Institute, MA 01609, USA. [3] Department of Biomedical Engineering, University of Connecticut, CT 06269, USA. [4] Department of Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA. [5] Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA. [6] Department of Biomedical Engineering, University of Connecticut, CT 06269, USA. Electronic address: ki.chon@uconn.edu. [7] Department of Biomedical Engineering, Worcester Polytechnic Institute, MA 01609, USA; Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, MA 01609, USA. Electronic address: ym@wpi.edu. [8] Department of Biomedical Engineering, Worcester Polytechnic Institute, MA 01609, USA; Vascular Biology Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Surgery, Harvard Medical School, Boston, MA 02115, USA. Electronic address: kwonmoo.lee@childrens.harvard.edu. [9]
DOI 10.1016/j.artmed.2023.102548
PMID 37210152
发布时间 2024-06-03
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Artificial intelligence in medicine

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