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Development and validation of a deep learning model for predicting postoperative survival of patients with gastric cancer

Bibliographic Data

ID15375800
AuthorsMengjie Wu (0000-0002-3997-8861, Zhengzhou University, corresponding author), Xiaofan Yang (0000-0001-7030-7516, Zhengzhou University), Yuxi Liu (0000-0003-2484-151X, Zhengzhou University), Feng Han (0000-0003-2933-6205, Zhengzhou University), Xi Li (0000-0003-3519-4805, Zhengzhou University), Jufeng Wang (0000-0002-5933-2152, Zhengzhou University), Dandan Guo (0000-0002-6681-0343, Third Affiliated Hospital of Zhengzhou University), Xiance Tang (Zhengzhou University), Lin Lü (0000-0003-0742-9072, Zhengzhou People's Hospital), Lu Lin (0000-0003-3313-354X), Changpeng Liu (0000-0002-5262-1808, Zhengzhou University)
Year2024
Volume24
Issue1
Pages723-723
Publication date2024-03-06
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBMC Public Health (JOURNAL)
Journal identifiersISSN: 1471-2458 • E-ISSN: 1471-2458
PublisherBioMed Central (PUBLISHER • GB)
DOI10.1186/s12889-024-18221-6
PMID38448849
OpenAlexW4392517299
LanguageEN
References cited31

DL-based model was established to accurately predict the survival rate of postoperative patients with GC

Biostatistics · Calibration · Cancer · Machine learning · Receiver operating characteristic · Stage (stratigraphy · Statistics · Survival analysis · Colorectal Cancer Screening and Detection · Computer Science · Gastric Cancer Management and Outcomes · Mathematics · Medicine · Radiomics and Machine Learning in Medical Imaging · Artificial Intelligence · Epidemiology · Internal Medicine

  • Deep learning in neural networks

    Open Access•Jurgen Schmidhuber•Neural Networks•2015

  • Global Cancer Statistics 2020

    Open Access•Hyuna Sung, Adalberto M Filho et al.•CA: A Cancer Journal for Clinicians•2021

Citation velocityhistorical
Highly citedNo

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