Correspondence to editorial on “Conventional and machine learning-based risk scores for patients with early- stage hepatocellular carcinoma”

키워드

참고문헌 (16)

1. Dai J, "Unlocking the future : Machine learning sheds light on prognostication for early-stage hepatocellular carcinoma : Editorial on"Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma"", Clin Mol Hepatol, 30, pp.698~701 (2024)
2. Lu MY, "Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients : A nationwide hepatitis C virus registry program", Clin Mol Hepatol, 30, pp.64~79 (2024)
3. Verma N, "Machine learning improves the prediction of significant fibrosis in Asian patients with metabolic dysfunction-associated steatotic liver disease-The Gut and Obesity in Asia(GO-ASIA)Study", Aliment Pharmacol Ther, 59, pp.774~788 (2024)
4. Kim HY, "An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B", J Hepatol, 76, pp.311~318 (2022)
5. Fan R, "Machinelearning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD-related liver fibrosis", Liver Int, 44, pp.749~759 (2024)
6. Kwong A, "Machine learning to predict waitlist dropout among liver transplant candidates with hepatocellular carcinoma", Cancer Med, 11, pp.1535~1541 (2022)
7. He ZL, "Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation", Hepatobiliary Pancreat Dis Int, 20, pp.222~231 (2021)
8. Roshanaei G, "Factors affecting the survival of patients with colorectal cancer using random survival forest", J Gastrointest Cancer, 53, pp.64~71 (2022)
9. Sapir-Pichhadze R, "Seeing the forest for the trees : Random forest models for predicting survival in kidney transplant recipients", Transplantation, 104, pp.905~906 (2020)
10. Huang H, "Decision tree model for predicting the overall survival of patients with diffused large B-cell lymphoma in the central nervous system", World Neurosurg, 166, pp.e189~e198 (2022)
11. Kivipelto M, "Risk score for the prediction of dementia risk in 20 years among middle aged people : a longitudinal, population-based study", Lancet Neurol, 5, pp.735~741 (2006)
12. Bae JM, "The clinical decision analysis using decision tree", Epidemiol Health, 36, pp.e2014025~ (2014)
13. Zhang Z, "Time-varying covariates and coefficients in Cox regression models", Ann Transl Med, 6, pp.121~ (2018)
14. Sterling RK, "Development of a simple noninvasive index to predict significant fibrosis in patients with HIV", HCV coinfection, 2006, pp.43, 1317~1325
15. Morales-Arráez D, "The MELD score is superior to the maddrey discriminant function score to predict shortterm mortality in alcohol-associated hepatitis : A global study", Am J Gastroenterol, 117, pp.301~310 (2022)
16. Ho CT, "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma", Clin Mol Hepatol, 30, pp.406~420 (2024)

관련 논문AI추천