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

Artificial Intelligence (1371) Machine Learning (1465) Clinical Information and Decision Making (1407) Innovations and Technology in Cancer Care (423) Innovations in Cancer Diagnostic and Decision Support (58)

Published on in Vol 23, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25053, first published .
Establishing Machine Learning Models to Predict Curative Resection in Early Gastric Cancer with Undifferentiated Histology: Development and Usability Study

Establishing Machine Learning Models to Predict Curative Resection in Early Gastric Cancer with Undifferentiated Histology: Development and Usability Study

Establishing Machine Learning Models to Predict Curative Resection in Early Gastric Cancer with Undifferentiated Histology: Development and Usability Study

Authors of this article:

Chang Seok Bang1, 2, 3, 4 Author Orcid Image ;   Ji Yong Ahn5 Author Orcid Image ;   Jie-Hyun Kim6 Author Orcid Image ;   Young-Il Kim7 Author Orcid Image ;   Il Ju Choi7 Author Orcid Image ;   Woon Geon Shin1, 2 Author Orcid Image
Article Authors Cited by (27) Tweetations (4) Metrics

Journals

  1. Kim J, Nam S, Park S. Usefulness of artificial intelligence in gastric neoplasms. World Journal of Gastroenterology 2021;27(24):3543 View
  2. Afrash M, Shanbehzadeh M, Kazemi-Arpanahi H. Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer. Clinical Medicine Insights: Oncology 2022;16 View
  3. Bang C, Lee J, Baik G. Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis. Journal of Medical Internet Research 2021;23(12):e33267 View
  4. Loh H, Ooi C, Seoni S, Barua P, Molinari F, Acharya U. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Computer Methods and Programs in Biomedicine 2022;226:107161 View
  5. Bang C. Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders. The Korean Journal of Helicobacter and Upper Gastrointestinal Research 2021;21(4):300 View
  6. Liu D, Wang X, Li L, Jiang Q, Li X, Liu M, Wang W, Shi E, Zhang C, Wang Y, Zhang Y, Wang L. Machine Learning-Based Model for the Prognosis of Postoperative Gastric Cancer. Cancer Management and Research 2022;Volume 14:135 View
  7. Yin M, Zhang R, Zhou Z, Liu L, Gao J, Xu W, Yu C, Lin J, Liu X, Xu C, Zhu J. Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals. Frontiers in Cellular and Infection Microbiology 2022;12 View
  8. Gong E, Bang C. Therapeutic approach to non-curative resection after endoscopic treatment in early gastric cancer. Journal of the Korean Medical Association 2022;65(5):284 View
  9. Kim H, Gong E, Bang C, Lee J, Suk K, Baik G. Computer-Aided Diagnosis of Gastrointestinal Protruded Lesions Using Wireless Capsule Endoscopy: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis. Journal of Personalized Medicine 2022;12(4):644 View
  10. Yun H, Huh C, Jung D, Lee G, Son N, Kim J, Youn Y, Park J, Shin S, Lee S, Lee Y. Machine Learning Improves the Prediction Rate of Non-Curative Resection of Endoscopic Submucosal Dissection in Patients with Early Gastric Cancer. Cancers 2022;14(15):3742 View
  11. Afrash M, Mirbagheri E, Mashoufi M, Kazemi-Arpanahi H. Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study. BMC Medical Informatics and Decision Making 2023;23(1) View
  12. Gong E, Bang C, Lee J, Baik G, Lim H, Jeong J, Choi S, Cho J, Kim D, Lee K, Shin S, Sigmund D, Moon B, Park S, Lee S, Bang K, Son D. Deep learning-based clinical decision support system for gastric neoplasms in real-time endoscopy: development and validation study. Endoscopy 2023;55(08):701 View
  13. Wang Z, Liu Y, Niu X. Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology. Seminars in Cancer Biology 2023;93:83 View
  14. Cai W, Xu J, Chen Y, Wu X, Zeng Y, Yu F. Performance of Machine Learning Algorithms for Predicting Disease Activity in Inflammatory Bowel Disease. Inflammation 2023;46(4):1561 View
  15. Guarneros-Nolasco L, Alor-Hernández G, Prieto-Avalos G, Sánchez-Cervantes J. Early Identification of Risk Factors in Non-Alcoholic Fatty Liver Disease (NAFLD) Using Machine Learning. Mathematics 2023;11(13):3026 View
  16. Ha S, Choi S, Lee S, Wijaya R, Kim J, Joo E, Kim J. Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study. Journal of Medical Internet Research 2023;25:e46520 View
  17. Zhang R, Yin M, Jiang A, Zhang S, Liu L, Xu X. Application Value of the Automated Machine Learning Model Based on Modified Computed Tomography Severity Index Combined With Serological Indicators in the Early Prediction of Severe Acute Pancreatitis. Journal of Clinical Gastroenterology 2024;58(7):692 View
  18. Shi J, Bendig D, Vollmar H, Rasche P. Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study. Journal of Medical Internet Research 2023;25:e45815 View
  19. Lee H, Nam K, Shin C, Lee H, Chang Y, Yoon H, Park Y, Kim N, Lee D, Ahn S, Kim H. Development and Validation of Models to Predict Lymph Node Metastasis in Early Gastric Cancer Using Logistic Regression and Gradient Boosting Machine Methods. Cancer Research and Treatment 2023;55(4):1240 View
  20. Kim H, Gong E, Bang C. Application of Machine Learning Based on Structured Medical Data in Gastroenterology. Biomimetics 2023;8(7):512 View
  21. Wang Y, Li N, Chen L, Wu M, Meng S, Dai Z, Zhang Y, Clarke M. Guidelines, Consensus Statements, and Standards for the Use of Artificial Intelligence in Medicine: Systematic Review. Journal of Medical Internet Research 2023;25:e46089 View
  22. Zhao Y, Pan X, Chen Y, Tan Y, Liu D. Lesion size affects the risk of technical difficulty in gastric endoscopic submucosal dissection. Scientific Reports 2024;14(1) View
  23. Meng L, Zhu P, Xia K. Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer. Frontiers in Public Health 2024;12 View
  24. Kim B, Kim B, Cho M, Chung H, Ryu J, Kim S. Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation. Scientific Reports 2024;14(1) View
  25. Zhang S, Xu H, Li W, Cui J, Zhao Q, Guo Z, Chen J, Yao Q, Li S, He Y, Qiao Q, Feng Y, Shi H, Song C. Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China. BMC Cancer 2024;24(1) View
  26. Cai W, Wu X, Chen Y, Chen J, Lin X. Risk Factors and Prediction of 28-Day-All Cause Mortality Among Critically Ill Patients with Acute Pancreatitis Using Machine Learning Techniques: A Retrospective Analysis of Multi-Institutions. Journal of Inflammation Research 2024;Volume 17:4611 View
  27. Mei Y, Gao J, Zhang B, Feng T, Wu W, Zhu Z, Zhu Z. Latest guideline of endoscopic submucosal dissection of early gastric cancer may not be suitable for Chinese patients: retrospective study findings from two centers. Surgical Endoscopy 2024;38(11):6726 View