152 related articles for article (PubMed ID: 38346963)
1. Risk factors affecting patients survival with colorectal cancer in Morocco: survival analysis using an interpretable machine learning approach.
El Badisy I; BenBrahim Z; Khalis M; Elansari S; ElHitmi Y; Abbass F; Mellas N; El Rhazi K
Sci Rep; 2024 Feb; 14(1):3556. PubMed ID: 38346963
[TBL] [Abstract][Full Text] [Related]
2. Risk factors affecting patients survival with colorectal cancer in Morocco : Survival Analysis using an Interpretable Machine Learning Approach.
El Badisy I; BenBrahim Z; Khalis M; Elansari S; ElHitmi Y; Abbas F; Mellas N; El Rhazi K
Res Sq; 2023 Jan; ():. PubMed ID: 36711858
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. Prognosis prediction for glioblastoma multiforme patients using machine learning approaches: Development of the clinically applicable model.
Kim Y; Kim KH; Park J; Yoon HI; Sung W
Radiother Oncol; 2023 Jun; 183():109617. PubMed ID: 36921767
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest.
Yang Y; Ma X; Wang Y; Ding X
Updates Surg; 2022 Feb; 74(1):355-365. PubMed ID: 34003477
[TBL] [Abstract][Full Text] [Related]
8. Machine Learning-Based Prognostic Model for Patients After Lung Transplantation.
Tian D; Yan HJ; Huang H; Zuo YJ; Liu MZ; Zhao J; Wu B; Shi LZ; Chen JY
JAMA Netw Open; 2023 May; 6(5):e2312022. PubMed ID: 37145595
[TBL] [Abstract][Full Text] [Related]
9. A Comparison Study of Machine Learning (Random Survival Forest) and Classic Statistic (Cox Proportional Hazards) for Predicting Progression in High-Grade Glioma after Proton and Carbon Ion Radiotherapy.
Qiu X; Gao J; Yang J; Hu J; Hu W; Kong L; Lu JJ
Front Oncol; 2020; 10():551420. PubMed ID: 33194609
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer: A Variable Importance Approach.
He J; Zhang JX; Chen CT; Ma Y; De Guzman R; Meng J; Pu Y
Med Care; 2020 May; 58(5):461-467. PubMed ID: 31985586
[TBL] [Abstract][Full Text] [Related]
13. [Application value of machine learning algorithms for predicting recurrence after resection of early-stage hepatocellular carcinoma].
Ji GW; Wang K; Xia YX; Li XC; Wang XH
Zhonghua Wai Ke Za Zhi; 2021 Aug; 59(8):679-685. PubMed ID: 34192861
[No Abstract] [Full Text] [Related]
14. Interpretable machine learning for predicting chronic kidney disease progression risk.
Zheng JX; Li X; Zhu J; Guan SY; Zhang SX; Wang WM
Digit Health; 2024; 10():20552076231224225. PubMed ID: 38235416
[TBL] [Abstract][Full Text] [Related]
15. Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model.
Farhadian M; Dehdar Karsidani S; Mozayanimonfared A; Mahjub H
BMC Cardiovasc Disord; 2021 Jan; 21(1):38. PubMed ID: 33461487
[TBL] [Abstract][Full Text] [Related]
16. Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma.
Wang D; Pan B; Huang JC; Chen Q; Cui SP; Lang R; Lyu SC
Front Oncol; 2023; 13():1106029. PubMed ID: 37007095
[TBL] [Abstract][Full Text] [Related]
17. Novel head and neck cancer survival analysis approach: random survival forests versus Cox proportional hazards regression.
Datema FR; Moya A; Krause P; Bäck T; Willmes L; Langeveld T; Baatenburg de Jong RJ; Blom HM
Head Neck; 2012 Jan; 34(1):50-8. PubMed ID: 21322080
[TBL] [Abstract][Full Text] [Related]
18. A Comprehensive Machine Learning Benchmark Study for Radiomics-Based Survival Analysis of CT Imaging Data in Patients With Hepatic Metastases of CRC.
Stüber AT; Coors S; Schachtner B; Weber T; Rügamer D; Bender A; Mittermeier A; Öcal O; Seidensticker M; Ricke J; Bischl B; Ingrisch M
Invest Radiol; 2023 Dec; 58(12):874-881. PubMed ID: 37504498
[TBL] [Abstract][Full Text] [Related]
19. Machine learning-based overall and cancer-specific survival prediction of M0 penile squamous cell carcinoma:A population-based retrospective study.
Chen D; Liang S; Chen J; Li K; Mi H
Heliyon; 2024 Jan; 10(1):e23442. PubMed ID: 38163093
[TBL] [Abstract][Full Text] [Related]
20. Use of survival support vector machine combined with random survival forest to predict the survival of nasopharyngeal carcinoma patients.
Xiao Z; Song Q; Wei Y; Fu Y; Huang D; Huang C
Transl Cancer Res; 2023 Dec; 12(12):3581-3590. PubMed ID: 38192980
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]