Acknowledgement
이 논문은 2021년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(No. NRF-2021R1I1A1A01059893). 연구비 제공자는 연구 설계, 데이터 수집 및 분석, 출판준비 및 결정에 아무런 역할을 하지 않았다.
References
- Yang C, Kors JA, Ioannou S, et al. Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review. J Am Med Inform Assoc 2022;29:983-989. https://doi.org/10.1093/jamia/ocac002
- van den Boorn HG, Engelhardt EG, van Kleef J, et al. Prediction models for patients with esophageal or gastric cancer: a systematic review and meta-analysis. PLoS One 2018;13:e0192310. https://doi.org/10.1371/journal.pone.0192310
- Backes Y, Schwartz MP, Ter Borg F, et al. Multicentre prospective evaluation of real-time optical diagnosis of T1 colorectal cancer in large non-pedunculated colorectal polyps using narrow band imaging (the OPTICAL study). Gut 2019;68:271-279. https://doi.org/10.1136/gutjnl-2017-314723
- Bae JS, Chang W, Kim SH, et al. Development of a predictive model for extragastric recurrence after curative resection for early gastric cancer. Gastric Cancer 2022;25: 255-264. https://doi.org/10.1007/s10120-021-01217-1
- Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.. Ann Intern Med 2015;162:55-63. https://doi.org/10.7326/m14-0697
- LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-444. https://doi.org/10.1038/nature14539
- Shrestha A, Mahmood A. Review of deep learning algorithms and architectures. IEEE Access 2019;7:53040-53065. https://doi.org/10.1109/ACCESS.2019.2912200
- Gong EJ, Bang CS, Lee JJ, et al. Deep learning-based clinical decision support system for gastric neoplasms in real-time endoscopy: development and validation study. Endoscopy 2023;55:701-708. https://doi.org/10.1055/a-2031-0691
- Agresti A. Categorical data analysis. Hoboken: John Wiley & Sons, 2012.
- Geng ZH, Zhu Y, Li QL, et al. Muscular injury as an independent risk factor for esophageal stenosis after endoscopic submucosal dissection of esophageal squamous cell cancer. Gastrointest Endosc 2023;98:534-542.e7. https://doi.org/10.1016/j.gie.2023.05.046
- Cox DR. Regression models and life-tables. J Royal Stat Soc Ser B 1972;34:187-220. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x
- George B, Seals S, Aban I. Survival analysis and regression models. J Nucl Cardiol 2014;21:686-694. https://doi. org/10.1007/s12350-014-9908-2
- Breiman L. Random forests. Mach Learn 2001;45:5-32. https://doi.org/10.1023/A:1010933404324
- Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. New York: Springer, 2009. p.587-604.
- Ziegler A, Konig IR. Mining data with random forests: current options for real-world applications. WIREs 2014;4:55-63. https://doi.org/10.1002/widm.1114
- Liwinski T, Casar C, Ruehlemann MC, et al. A diseasespecific decline of the relative abundance of Bifidobacterium in patients with autoimmune hepatitis. Aliment Pharmacol Ther 2020;51:1417-1428. https://doi.org/10.1111/apt.15754
- Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Statist 2001;95:1189-1232. https://doi.org/10.1214/aos/1013203451
- Chen T, He T, Benesty M, et al. Xgboost: extreme gradient boosting. R package version 0.4-2. 2015. https://xgboost.readthedocs.io/en/stable/R-package/xgboostPresentation.html (accessed Oct 1, 2023).
- Ke G, Meng Q, Finley T, et al. LightGBM: a highly efficient gradient boosting decision tree [abstract]. In: Proceedings of the 31st International Conference on Neural Information Processing Systems; 2017 Dec 4-9; Long Beach, USA. p.3149-3157.
- Sagi O, Rokach L. Approximating XGBoost with an interpretable decision tree. Inf Sci 2021;572:522-542. https://doi.org/10.1016/j.ins.2021.05.055
- Kwon Y, Kwon JW, Ha J, et al. Remission of type 2 diabetes after gastrectomy for gastric cancer: diabetes prediction score. Gastric Cancer 2022;25:265-274. https://doi.org/10.1007/s10120-021-01216-2
- Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Stat Soc Ser B 1996;58:267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
- Hastie T, Tibshirani R, Wainwright M. Statistical learning with sparsity: the lasso and generalizations. Boca Raton: CRC Press, 2015.
- Zou H, Hastie T. Regularization and variable selection via the elastic net. J Royal Stat Soc Ser B 2005;67:301-320. https://doi.org/10.1111/j.1467-9868.2005.00503.x
- Ali H, Patel P, Malik TF, et al. Endoscopic sleeve gastroplasty reintervention score using supervised machine learning. Gastrointest Endosc 2023;98:747-754.e5. https://doi.org/10.1016/j.gie.2023.05.059
- Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. Support vector machines. IEEE Intell Syst Appl 1998;13:18-28. https://doi.org/10.1109/5254.708428
- Chen PH, Lin CJ, Scholkopf B. A tutorial on ν-support vector machines. Appl Stoch Models Bus Ind 2005;21:111-136. https://doi.org/10.1002/asmb.537
- Salcedo-Sanz S, Rojo-Alvarez JL, Martinez-Ramon M, Camps-Valls G. Support vector machines in engineering: an overview. WIREs 2014;4:234-267. https://doi.org/10.1002/widm.1125
- Yu S, Li Y, Liao Z, et al. Plasma extracellular vesicle long RNA profiling identifies a diagnostic signature for the detection of pancreatic ductal adenocarcinoma. Gut 2020;69:540-550. https://doi.org/10.1136/gutjnl-2019-318860
- Bleeker SE, Moll HA, Steyerberg EW, et al. External validation is necessary in prediction research: a clinical example. J Clin Epidemiol 2003;56:826-832. https://doi.org/10.1016/s0895-4356(03)00207-5
- Xu Y, Goodacre R. On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. J Anal Test 2018;2:249-262. https://doi.org/10.1007/s41664-018-0068-2
- Gholamy A, Kreinovich V, Kosheleva O. Why 70/30 or 80/20 relation between training and testing sets: a pedagogical explanation. 2018 Feb. Report No.: UTEP-CS-18-09.
- Prechelt L. Early stopping-but when? In: Orr GB, Muller KR, eds. Neural networks: tricks of the trade. Berlin, Heidelberg: Springer, 2002:55-69.
- Berrar D. Cross-validation. Encycl Bioinform Comput Biol 2019;1:542-545. https://doi.org/10.1016/B978-0-12-809633-8.20349-X
- Efron B, Tibshirani RJ. An introduction to the bootstrap. New York: Chapman and Hall, 1994.
- Harrell FE. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Cham: Springer International Publishing, 2015.
- Kuhn M, Johnson K. Applied predictive modeling. New York: Springer, 2013.
- Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ 2020;368:m441. https://doi.org/10.1136/bmj.m441
- Van Calster B, Steyerberg EW, Wynants L, van Smeden M. There is no such thing as a validated prediction model. BMC Med 2023;21:70. https://doi.org/10.1186/s12916-023-02779-w
- Moons KG, Altman DG, Reitsma JB, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015;162:W1-W73. https://doi.org/10.7326/M14-0698
- Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet 2019;393:1577-1579. https://doi.org/10.1016/S0140-6736(19)30037-6