239 related articles for article (PubMed ID: 33003533)
1. Comparison of the Tree-Based Machine Learning Algorithms to Cox Regression in Predicting the Survival of Oral and Pharyngeal Cancers: Analyses Based on SEER Database.
Du M; Haag DG; Lynch JW; Mittinty MN
Cancers (Basel); 2020 Sep; 12(10):. PubMed ID: 33003533
[TBL] [Abstract][Full Text] [Related]
2. Which model is better in predicting the survival of laryngeal squamous cell carcinoma?: Comparison of the random survival forest based on machine learning algorithms to Cox regression: analyses based on SEER database.
Sun H; Wu S; Li S; Jiang X
Medicine (Baltimore); 2023 Mar; 102(10):e33144. PubMed ID: 36897699
[TBL] [Abstract][Full Text] [Related]
3. The prediction of the survival in patients with severe trauma during prehospital care: Analyses based on NTDB database.
Peng C; Peng L; Yang F; Yu H; Chen Q; Guo Y; Xu S; Jin Z
Eur J Trauma Emerg Surg; 2024 Mar; ():. PubMed ID: 38483558
[TBL] [Abstract][Full Text] [Related]
4. Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study.
Yang X; Qiu H; Wang L; Wang X
J Med Internet Res; 2023 Oct; 25():e44417. PubMed ID: 37883174
[TBL] [Abstract][Full Text] [Related]
5. The prognostic value of machine learning techniques versus cox regression model for head and neck cancer.
Peng J; Lu Y; Chen L; Qiu K; Chen F; Liu J; Xu W; Zhang W; Zhao Y; Yu Z; Ren J
Methods; 2022 Sep; 205():123-132. PubMed ID: 35798257
[TBL] [Abstract][Full Text] [Related]
6. Prediction of survival in oropharyngeal squamous cell carcinoma using machine learning algorithms: A study based on the surveillance, epidemiology, and end results database.
Kim SI; Kang JW; Eun YG; Lee YC
Front Oncol; 2022; 12():974678. PubMed ID: 36072804
[TBL] [Abstract][Full Text] [Related]
7. Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis.
Yan L; Gao N; Ai F; Zhao Y; Kang Y; Chen J; Weng Y
Front Oncol; 2022; 12():967758. PubMed ID: 36072795
[TBL] [Abstract][Full Text] [Related]
8. Development and validation of survival prediction model for gastric adenocarcinoma patients using deep learning: A SEER-based study.
Zeng J; Li K; Cao F; Zheng Y
Front Oncol; 2023; 13():1131859. PubMed ID: 36959782
[TBL] [Abstract][Full Text] [Related]
9. Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort.
Hao Y; Liang D; Zhang S; Wu S; Li D; Wang Y; Shi M; He Y
Biomol Biomed; 2023 Sep; 23(5):883-893. PubMed ID: 36967662
[TBL] [Abstract][Full Text] [Related]
10. Dementia risk prediction in individuals with mild cognitive impairment: a comparison of Cox regression and machine learning models.
Wang M; Greenberg M; Forkert ND; Chekouo T; Afriyie G; Ismail Z; Smith EE; Sajobi TT
BMC Med Res Methodol; 2022 Nov; 22(1):284. PubMed ID: 36324086
[TBL] [Abstract][Full Text] [Related]
11. A comparative study of forest methods for time-to-event data: variable selection and predictive performance.
Liu Y; Zhou S; Wei H; An S
BMC Med Res Methodol; 2021 Sep; 21(1):193. PubMed ID: 34563138
[TBL] [Abstract][Full Text] [Related]
12. Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma.
Xu L; Cai L; Zhu Z; Chen G
BMC Endocr Disord; 2023 Jun; 23(1):129. PubMed ID: 37291551
[TBL] [Abstract][Full Text] [Related]
13. Multiple Machine Learnings Revealed Similar Predictive Accuracy for Prognosis of PNETs from the Surveillance, Epidemiology, and End Result Database.
Song Y; Gao S; Tan W; Qiu Z; Zhou H; Zhao Y
J Cancer; 2018; 9(21):3971-3978. PubMed ID: 30410601
[No Abstract] [Full Text] [Related]
14. Comparison of machine learning algorithms for the prediction of five-year survival in oral squamous cell carcinoma.
Alkhadar H; Macluskey M; White S; Ellis I; Gardner A
J Oral Pathol Med; 2021 Apr; 50(4):378-384. PubMed ID: 33220109
[TBL] [Abstract][Full Text] [Related]
15. The Application and Comparison of Machine Learning Models for the Prediction of Breast Cancer Prognosis: Retrospective Cohort Study.
Xiao J; Mo M; Wang Z; Zhou C; Shen J; Yuan J; He Y; Zheng Y
JMIR Med Inform; 2022 Feb; 10(2):e33440. PubMed ID: 35179504
[TBL] [Abstract][Full Text] [Related]
16. An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning.
Senders JT; Staples P; Mehrtash A; Cote DJ; Taphoorn MJB; Reardon DA; Gormley WB; Smith TR; Broekman ML; Arnaout O
Neurosurgery; 2020 Feb; 86(2):E184-E192. PubMed ID: 31586211
[TBL] [Abstract][Full Text] [Related]
17. Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database.
Jiang C; Wang K; Yan L; Yao H; Shi H; Lin R
Cancer Med; 2023 Jun; 12(11):12413-12424. PubMed ID: 37165971
[TBL] [Abstract][Full Text] [Related]
18. Comparison of time-to-event machine learning models in predicting oral cavity cancer prognosis.
Adeoye J; Hui L; Koohi-Moghadam M; Tan JY; Choi SW; Thomson P
Int J Med Inform; 2022 Jan; 157():104635. PubMed ID: 34800847
[TBL] [Abstract][Full Text] [Related]
19. Does the SORG Algorithm Predict 5-year Survival in Patients with Chondrosarcoma? An External Validation.
Bongers MER; Thio QCBS; Karhade AV; Stor ML; Raskin KA; Lozano Calderon SA; DeLaney TF; Ferrone ML; Schwab JH
Clin Orthop Relat Res; 2019 Oct; 477(10):2296-2303. PubMed ID: 31107338
[TBL] [Abstract][Full Text] [Related]
20. [Efficacy of machine learning models
Gao K; Wang Y; Cao H; Jia J
Nan Fang Yi Ke Da Xue Xue Bao; 2023 Jun; 43(6):952-963. PubMed ID: 37439167
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]