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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
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Last data update: Apr 17, 2024
. (Total: 63598 Documents since 2012)
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Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach.
Bruno Casaes Teixeira et al. Int J Public Health 2023 681604789
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Machine learning applications for early detection of esophageal cancer: a systematic review.
Farhang Hosseini et al. BMC Med Inform Decis Mak 2023 23(1) 124
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The current state of artificial intelligence in endoscopic diagnosis of early esophageal squamous cell carcinoma.
Yuwei Pan et al. Front Oncol 2023 131198941
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Evaluation of Endoscopic Response Using Deep Neural Network in Esophageal Cancer Patients Who Received Neoadjuvant Chemotherapy.
Satoru Matsuda et al. Annals of surgical oncology 2023
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Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis.
Islam Md Mohaimenul et al. Cancers 2022 14(23)
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Development and validation of a deep learning model to predict survival of patients with esophageal cancer.
Huang Chen et al. Frontiers in oncology 2022 12971190
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Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer.
Gong Xian et al. Journal of thoracic disease 2022 13(11) 6240-6251
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Real-time autOmatically updated data warehOuse in healThcare (ROOT): an innovative and automated data collection system.
Jung Hyun Ae et al. Translational lung cancer research 2021 10(10) 3865-3874
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Automatic Recognition of Colon and Esophagogastric Cancer with Machine Learning and Hyperspectral Imaging.
Collins Toby et al. Diagnostics (Basel, Switzerland) 2021 11(10)
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Identifying Individualized Risk Profiles for Radiotherapy-Induced Lymphopenia Among Patients With Esophageal Cancer Using Machine Learning.
Zhu Cong et al. JCO clinical cancer informatics 2021 51044-1053
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Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review.
Kuntz Sara et al. European journal of cancer (Oxford, England : 1990) 2021 155200-215
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Diagnostic Performance of Artificial Intelligence-Based Models for the Detection of Early Esophageal Cancers in Barret's Esophagus: A Meta-Analysis of Patient-Based Studies.
Bhatti Khalid M et al. Cureus 2021 13(6) e15447
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Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature.
Xie Chen-Yi et al. Cancers 2021 13(10)
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Computer-aided diagnosis of esophageal cancer and neoplasms in endoscopic images: a systematic review and meta-analysis of diagnostic test accuracy.
Bang Chang Seok et al. Gastrointestinal endoscopy 2020 Dec
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Artificial Intelligence and Its Role in Identifying Esophageal Neoplasia.
Syed Taseen et al. Digestive diseases and sciences 2020 Oct
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Artificial intelligence-assisted esophageal cancer management: Now and future.
Zhang Yu-Hang et al. World journal of gastroenterology 2020 Sep 26(35) 5256-5271
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Learning spatiotemporal features for esophageal abnormality detection from endoscopic videos.
Ghatwary Noha et al. IEEE journal of biomedical and health informatics 2020 May PP
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Improving Temporal Stability and Accuracy for Endoscopic Video Tissue Classification Using Recurrent Neural Networks.
Boers Tim et al. Sensors (Basel, Switzerland) 2020 Jul 20(15)
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Utilizing artificial intelligence in endoscopy: a clinician's guide.
Namikawa Ken et al. Expert review of gastroenterology & hepatology 2020 Jun
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Spatial distribution of esophageal cancer mortality in China: a machine learning approach.
Liao Yilan et al. International health 2020 May
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Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks.
Horie Yoshimasa et al. Gastrointestinal endoscopy 2019 89(1) 25-32
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 17, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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