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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)
Last data update: Apr 25, 2024
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The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information.
Tiantian Wu et al. Clin Nutr 2024 43(5) 1151-1161
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Is Risk-Stratifying Patients with Colorectal Cancer Using a Deep Learning-Based Prognostic Biomarker Cost-Effective?
Anna Kenseth et al. Pharmacoeconomics 2024
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Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models.
Shayeste Alinia et al. Heliyon 2024 10(6) e27854
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Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests.
Huijie Wang et al. Front Oncol 2024 141325514
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Commentary: An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer.
Katsuro Ichimasa et al. Front Oncol 2024 141337576
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Machine learning-based classifiers to predict metastasis in colorectal cancer patients.
Raheleh Talebi et al. Front Artif Intell 2024 71285037
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A systematic evaluation of big data-driven colorectal cancer studies.
Eslam Bani Mohammad et al. Med Glas (Zenica) 2024 21(1) 63-77
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Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study.
Gohar Mohammadi et al. Asian Pac J Cancer Prev 2024 25(1) 333-342
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Prediction of Chemotherapy Efficacy in Patients with Colorectal Cancer Ovarian Metastases: A Preliminary Study Using Contrast-Enhanced Computed-Tomography-Based Radiomics.
Jinghan Yu et al. Diagnostics (Basel) 2024 14(1)
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Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer.
Kriti Das et al. Explor Target Antitumor Ther 2024 4(6) 1286-1300
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Prediction of early-onset colorectal cancer mortality rates in the United States using machine learning.
Hassam Ali et al. Cancer Med 2023
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Development and validation of an artificial intelligence prediction model and a survival risk stratification for lung metastasis in colorectal cancer from highly imbalanced data: A multicenter retrospective study.
Weiyuan Zhang et al. Eur J Surg Oncol 2023 49(12) 107107
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Use of artificial intelligence in the management of T1 colorectal cancer: a new tool in the arsenal or is deep learning out of its depth?
James Weiquan Li et al. Clin Endosc 2023
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A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer.
Kamyab Keshtkar et al. Turk J Gastroenterol 2023
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An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration.
Jia Li et al. Cancer Med 2023
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Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients.
Hui Zhu et al. Cancer Imaging 2023 23(1) 74
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Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural Networks.
Vahab Vahdat et al. Med Decis Making 2023 272989X231184175
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The use of artificial intelligence to identify subjects with a positive FOBT predicted to be non-compliant with both colonoscopy and harbor cancer.
Tom Konikoff et al. Dig Liver Dis 2023
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Deep Learning Empowers Endoscopic Detection and Polyps Classification: A Multiple-Hospital Study.
Ming-Hung Shen et al. Diagnostics (Basel) 2023 13(8)
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Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?
Antonio Z Gimeno-García et al. Cancers (Basel) 2023 15(8)
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Prescription Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Incidence of Depression Among Older Cancer Survivors With Osteoarthritis: A Machine Learning Analysis.
Nazneen Fatima Shaikh et al. Cancer Inform 2023 2211769351231165161
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Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect.
Zugang Yin et al. Frontiers in medicine 2023 101128084
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Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening.
Marco Spadaccini et al. Diagnostics (Basel, Switzerland) 2023 13(6)
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Artificial Intelligence in Pediatric Endoscopy: Current Status and Future Applications.
Jasbir Dhaliwal et al. Gastrointestinal endoscopy clinics of North America 2023 33(2) 291-308
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Development and validation of machine learning models for postoperative venous thromboembolism prediction in colorectal cancer inpatients: a retrospective study.
Li Qin et al. Journal of gastrointestinal oncology 2023 14(1) 220-232
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Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image.
Lin Pan et al. Abdominal radiology (New York) 2023
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A Real-Time Polyp-Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks.
Adrian Krenzer et al. Journal of imaging 2023 9(2)
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Computer-Assisted Diagnosis of Lymph Node Metastases in Colorectal Cancers Using Transfer Learning With an Ensemble Model.
Amjad Khan et al. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 2023 36(5) 100118
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A machine learning tool for identifying non-metastatic colorectal cancer in primary care.
Elinor Nemlander et al. European journal of cancer (Oxford, England : 1990) 2023 182100-106
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Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques.
Khaled ELKarazle et al. Sensors (Basel, Switzerland) 2023 23(3)
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 25, 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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