[1] BURG ML, DANESHMAND S. Frailty and preoperative risk assessment before radical cystectomy[J]. Curr Opin Urol, 2019, 29(3): 216. doi: 10.1097/MOU.0000000000000616
[2] MATSUMOTO S, TAKAYAMA T, WAKATSUKI K, et al. Preoperative cardiac risk assessment and surgical outcomes of patients with gastric cancer[J]. Ann Surg Oncol, 2016, 23(Suppl 2): S222.
[3] LEYH-BANNURAH SR, DELL'OGLIO P, ZAFFUTO E, et al. Assessment of oncological outcomes after radical prostatectomy according to preoperative and postoperative cancer of the prostate risk assessment scores: results from a large, two-center experience[J]. Eur Urol Focus, 2019, 5(4): 568. doi: 10.1016/j.euf.2017.10.015
[4] GUO R, YU W, MENG Y, et al. Correlation of ASA Grade and the Charlson Comorbidity Index With Complications in Patients After Transurethral Resection of Prostate[J]. Urology, 2016, 98: 120. doi: 10.1016/j.urology.2016.07.025
[5] LAMBDEN S, LATERRE PF, LEVY MM, et al. The SOFA score-development, utility and challenges of accurate assessment in clinical trials[J]. Crit Care, 2019, 23(1): 374. doi: 10.1186/s13054-019-2663-7
[6] HANSTED AK, MØLLER MH, MØLLER AM, et al. APACHE Ⅱ score validation in emergency abdominal surgery. A post hoc analysis of the InCare trial[J]. Acta Anaesthesiol Scand, 2020, 64(2): 180. doi: 10.1111/aas.13476
[7] AUCOIN S, MCISAAC DI. Emergency general surgery in older adults: a review[J]. Anesthesiol Clin, 2019, 37(3): 493. doi: 10.1016/j.anclin.2019.04.008
[8] HUANG S, YANG J, FONG S, et al. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges[J]. Cancer Lett, 2020, 471: 61. doi: 10.1016/j.canlet.2019.12.007
[9] LI B, DING S, SONG G, et al. Computer-aided diagnosis and clinical trials of cardiovascular diseases based on artificial intelligence technologies for risk-early warning model[J]. J Med Syst, 2019, 43(7): 228. doi: 10.1007/s10916-019-1346-x
[10] KIM J, CHAE M, CHANG HJ, et al. Predicting Cardiac Arrest and Respiratory Failure Using Feasible Artificial Intelligence with Simple Trajectories of Patient Data[J]. J Clin Med, 2019, 8(9): 1336. doi: 10.3390/jcm8091336
[11] JIANG H, GU J, DU J, et al. A 21-gene Support Vector Machine classifier and a 10-gene risk score system constructed for patients with gastric cancer[J]. Mol Med Rep, 2020, 21(1): 347.
[12] GOLPOUR P, GHAYOUR-MOBARHAN M, SAKI A, et al. Comparison of Support Vector Machine, NaÏve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography[J]. Int J Environ Res Public Health, 2020, 17(18): 6449. doi: 10.3390/ijerph17186449
[13] OBUCHOWSKI NA, BULLEN JA. Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine[J]. Phys Med Biol, 2018, 63(7): 07TR01.
[14] CARTER JV, PAN J, RAI SN, et al. ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves[J]. Surgery, 2016, 159(6): 1638. doi: 10.1016/j.surg.2015.12.029
[15] MEURER WJ, TOLLES J. Logistic Regression Diagnostics: Understanding How Well a Model Predicts Outcomes[J]. JAMA, 2017, 317(10): 1068. doi: 10.1001/jama.2016.20441
[16] DE BOVES HARRINGTON P. Support vector machine classification trees based on fuzzy entropy of classification[J]. Anal Chim Acta, 2017, 954: 14. doi: 10.1016/j.aca.2016.11.072
[17] ING E, SU W, SCHONLAU M, et al. Support Vector Machines and logistic regression to predict temporal artery biopsy outcomes[J]. Can J Ophthalmol, 2019, 54(1): 116. doi: 10.1016/j.jcjo.2018.05.006
[18] CHEN K, LI R, DOU Y, et al. Ranking support vector machine with kernel approximation[J]. Comput Intell Neurosci, 2017, 2017: 4629534.
[19] JIANG Y, XIE J, HAN Z, et al. Immunomarker support vector machine classifier for prediction of gastric cancer survival and adjuvant chemotherapeutic benefit[J]. Clin Cancer Res, 2018, 24(22): 5574. doi: 10.1158/1078-0432.CCR-18-0848
[20] ZHI J, SUN J, WANG Z, et al. Support vector machine classifier for prediction of the metastasis of colorectal cancer[J]. Int J Mol Med, 2018, 41(3): 1419.