This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1915) Machine Learning (2076) Secondary Use of Clinical Data for Research and Surveillance (474) Colon and Rectal Cancer (50) Artificial Intelligence (2129) Decision Support for Health Professionals (1539)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44417, first published .
Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study

Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study

Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study

Authors of this article:

Xulin Yang1 Author Orcid Image ;   Hang Qiu1, 2 Author Orcid Image ;   Liya Wang2 Author Orcid Image ;   Xiaodong Wang3 Author Orcid Image
Article Authors Cited by (31) Tweetations (3) Metrics

Journals

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  2. Qi H, Hu Y, Fan R, Deng L. Tab-Cox: An Interpretable Deep Survival Analysis Model for Patients With Nasopharyngeal Carcinoma Based on TabNet. IEEE Journal of Biomedical and Health Informatics 2024;28(8):4937 View
  3. Yang P, Chen W, Qiu H. MMGCN: Multi-modal multi-view graph convolutional networks for cancer prognosis prediction. Computer Methods and Programs in Biomedicine 2024;257:108400 View
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  7. Li Z, Aihemaiti Y, Yang Q, Ahemai Y, Li Z, Du Q, Wang Y, Zhang H, Cai Y. Survival machine learning model of T1 colorectal postoperative recurrence after endoscopic resection and surgical operation: a retrospective cohort study. BMC Cancer 2025;25(1) View
  8. Tang W, Mo S, Xie Y, Wei T, Chen G, Teng Y, Jia K. Predicting Overall Survival in Patients with Male Breast Cancer: Nomogram Development and External Validation Study. JMIR Cancer 2025;11:e54625 View
  9. Jin Y, Zhao M, Su T, Fan Y, Ouyang Z, Lv F. Comparing Random Survival Forests and Cox Regression for Nonresponders to Neoadjuvant Chemotherapy Among Patients With Breast Cancer: Multicenter Retrospective Cohort Study. Journal of Medical Internet Research 2025;27:e69864 View
  10. Yang X, Qiu H. Deep Gated Neural Network With Self-Attention Mechanism for Survival Analysis. IEEE Journal of Biomedical and Health Informatics 2025;29(4):2945 View
  11. Wang X, Chen H, Wang L, Sun W. Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES. BMC Gastroenterology 2025;25(1) View
  12. Zhou Y, Zhao J, Zou F, Tan Y, Zeng W, Jiang J, Hu J, Zeng Q, Gong L, Liu L, Zhong L. Interpretable machine learning models based on body composition and inflammatory nutritional index (BCINI) to predict early postoperative recurrence of colorectal cancer: Multi-center study. Computer Methods and Programs in Biomedicine 2025;269:108874 View
  13. Yao F, Miao J, Quan B, Li J, Tang B, Lu S, Yin X. Predicting Resistance and Survival of HCC Patients Post-HAIC: Based on Shapley Additive exPlanations and Machine Learning. Journal of Hepatocellular Carcinoma 2025;Volume 12:1111 View
  14. Pu J, Zhou B, Yao Y, Wu Z, Wen Y, Xu R, Xu H. Development and Validation of a Lifestyle-Based 10-Year Risk Prediction Model of Colorectal Cancer for Early Stratification: Evidence from a Longitudinal Screening Cohort in China. Nutrients 2025;17(11):1898 View
  15. Luo H, Yu X, Li X, Yin D, Cao Y. Development and validation of a Log odds of negative lymph nodes/T stage ratio-based prognostic model for gastric cancer. Frontiers in Oncology 2025;15 View
  16. Nhu N, Kang J, Yeh T, Chang J, Tzeng Y, Chan T, Wu C, Lam C. Predicting 14-day readmission in middle-aged and elderly patients with pneumonia using emergency department data: a multicentre retrospective cohort study with a survival machine learning approach. BMJ Open 2025;15(6):e102711 View
  17. Chen Z, Wang C, Wang F. Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice. World Journal of Gastroenterology 2025;31(24) View
  18. Luo H, Liu X, Yang Y, Tang B, He P, Ding L, Wang Z, Shi J. Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble. Scientific Reports 2025;15(1) View
  19. Yang X, Qiu H. Multimodal representation learning with hierarchical knowledge decomposition for cancer survival analysis. Neurocomputing 2025;652:131053 View
  20. Li Z, Wang J, Zhang Y, Yang Z, Zhou F, Bai X, Zhang Q, Zhen W, Xu R, Wu W, Yao Z, Li X, Yang Y. Predicting the prognosis of epithelial ovarian cancer patients based on deep learning models. Frontiers in Oncology 2025;15 View
  21. Wang J, Li Q, Xie C, Li X, Wang H, Xu W, Lv R, Zhai X, Xu P, Li K, Song X. Predicting In-Hospital Mortality in Intensive Care Unit Patients Using Causal SurvivalNet With Serum Chloride and Other Causal Factors: Cross-Country Study. Journal of Medical Internet Research 2025;27:e70118 View
  22. Farhoudian A, Heidari A, Shahhosseini R. A new era in colorectal cancer: Artificial Intelligence at the forefront. Computers in Biology and Medicine 2025;196:110926 View
  23. Oh S, Lee Y, Baek J, Sunwoo W. Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study. JMIR Medical Informatics 2025;13:e75022 View
  24. Zhao X, Zhang Y, Fan Q, He Y, Ma Y, Sun M, Zhao Y, Jiang Y, Jia D. Exploring potential associations and biomarkers linked polycystic ovarian syndrome with atherosclerosis via comprehensive bioinformatics analysis, machine learning, and animal experiments. Functional & Integrative Genomics 2025;25(1) View
  25. Park S, Yeo N, Kim T, Lim M, Yim I, Kwon O, Nam S, Lee H, Kim W. Explainable AI for colorectal cancer mortality and risk factor prediction in Korea: A nationwide cancer cohort study. International Journal of Medical Informatics 2026;205:106125 View
  26. Li J, Hu X, Pan C, Liu Q, Zhang S, Zhang C, Zhou X. Identification and validation of lactate-related gene signatures in endometriosis for clinical evaluation and immune characterization by WGCNA and machine learning. Frontiers in Cell and Developmental Biology 2025;13 View
  27. Feng Z, Tan Y, Gui M, Chen C, Wang Y, Yang Z, Wu S, Xue Y, Zhao W, Wang Z, Yu K, Deng H, Liu X. Association of blood inflammatory biomarkers with the incidence of atrial fibrillation among senior adults: An 8-year prospective cohort study. Heart Rhythm 2025 View
  28. Jahani S, Roshanaei G, Tapak L. Assessing the accuracy of survival machine learning and traditional statistical models for Alzheimer's disease prediction over time: a study on the ADNI cohort. BMC Medical Research Methodology 2025;25(1) View
  29. Chen S, Zhang J, Shang-Guan X, He T, Wang H, Wang S, Zheng S, Huang L, Chen X. Development of a preoperative predictive classifier and tailored staging system for obstructive colorectal cancer. BMC Surgery 2025;25(1) View
  30. Abdulaeva R, Pavlova V, Gevorkyan T, Belenkaya Y, Manukyan M, Gordeev S. Artificial intelligence for predicting long-term outcomes in patients with colorectal cancer (a systematic review and meta-analysis). Koloproktologia 2025;24(4):125 View
  31. Yalçıner M, Erdat E, Kavak E, Utkan G. Development and validation of an AI-augmented deep learning model for survival prediction in de novo metastatic colorectal cancer. Discover Oncology 2025;16(1) View