136 related articles for article (PubMed ID: 24950066)
1. Prediction of survival with alternative modeling techniques using pseudo values.
van der Ploeg T; Datema F; Baatenburg de Jong R; Steyerberg EW
PLoS One; 2014; 9(6):e100234. PubMed ID: 24950066
[TBL] [Abstract][Full Text] [Related]
2. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints.
van der Ploeg T; Austin PC; Steyerberg EW
BMC Med Res Methodol; 2014 Dec; 14():137. PubMed ID: 25532820
[TBL] [Abstract][Full Text] [Related]
3. Modern modeling techniques had limited external validity in predicting mortality from traumatic brain injury.
van der Ploeg T; Nieboer D; Steyerberg EW
J Clin Epidemiol; 2016 Oct; 78():83-89. PubMed ID: 26987507
[TBL] [Abstract][Full Text] [Related]
4. Prediction of survival in patients with head and neck cancer.
Baatenburg de Jong RJ; Hermans J; Molenaar J; Briaire JJ; le Cessie S
Head Neck; 2001 Sep; 23(9):718-24. PubMed ID: 11505480
[TBL] [Abstract][Full Text] [Related]
5. A reliable method for colorectal cancer prediction based on feature selection and support vector machine.
Zhao D; Liu H; Zheng Y; He Y; Lu D; Lyu C
Med Biol Eng Comput; 2019 Apr; 57(4):901-912. PubMed ID: 30478811
[TBL] [Abstract][Full Text] [Related]
6. Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck.
Bryce TJ; Dewhirst MW; Floyd CE; Hars V; Brizel DM
Int J Radiat Oncol Biol Phys; 1998 May; 41(2):339-45. PubMed ID: 9607349
[TBL] [Abstract][Full Text] [Related]
7. Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.
Paliwal N; Jaiswal P; Tutino VM; Shallwani H; Davies JM; Siddiqui AH; Rai R; Meng H
Neurosurg Focus; 2018 Nov; 45(5):E7. PubMed ID: 30453461
[TBL] [Abstract][Full Text] [Related]
8. SVM-RFE: selection and visualization of the most relevant features through non-linear kernels.
Sanz H; Valim C; Vegas E; Oller JM; Reverter F
BMC Bioinformatics; 2018 Nov; 19(1):432. PubMed ID: 30453885
[TBL] [Abstract][Full Text] [Related]
9. Prediction of intracranial findings on CT-scans by alternative modelling techniques.
van der Ploeg T; Smits M; Dippel DW; Hunink M; Steyerberg EW
BMC Med Res Methodol; 2011 Oct; 11():143. PubMed ID: 22026551
[TBL] [Abstract][Full Text] [Related]
10. Development and validation of machine learning prediction model based on computed tomography angiography-derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study.
Chen G; Lu M; Shi Z; Xia S; Ren Y; Liu Z; Liu X; Li Z; Mao L; Li XL; Zhang B; Zhang LJ; Lu GM
Eur Radiol; 2020 Sep; 30(9):5170-5182. PubMed ID: 32350658
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. External Validation of Models for Predicting Disability in Community-Dwelling Older People in the Netherlands: A Comparative Study.
van der Ploeg T; Schalk R; Gobbens RJJ
Clin Interv Aging; 2023; 18():1873-1882. PubMed ID: 38020449
[TBL] [Abstract][Full Text] [Related]
13. A Comparison of Different Modeling Techniques in Predicting Mortality With the Tilburg Frailty Indicator: Longitudinal Study.
van der Ploeg T; Gobbens R
JMIR Med Inform; 2022 Mar; 10(3):e31480. PubMed ID: 35353054
[TBL] [Abstract][Full Text] [Related]
14. Prediction of lung cancer patient survival via supervised machine learning classification techniques.
Lynch CM; Abdollahi B; Fuqua JD; de Carlo AR; Bartholomai JA; Balgemann RN; van Berkel VH; Frieboes HB
Int J Med Inform; 2017 Dec; 108():1-8. PubMed ID: 29132615
[TBL] [Abstract][Full Text] [Related]
15. Improved survival of patients with human papillomavirus-positive head and neck squamous cell carcinoma in a prospective clinical trial.
Fakhry C; Westra WH; Li S; Cmelak A; Ridge JA; Pinto H; Forastiere A; Gillison ML
J Natl Cancer Inst; 2008 Feb; 100(4):261-9. PubMed ID: 18270337
[TBL] [Abstract][Full Text] [Related]
16. A two-stage modeling approach for breast cancer survivability prediction.
Sedighi-Maman Z; Mondello A
Int J Med Inform; 2021 May; 149():104438. PubMed ID: 33730681
[TBL] [Abstract][Full Text] [Related]
17. Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries.
Feng JZ; Wang Y; Peng J; Sun MW; Zeng J; Jiang H
J Crit Care; 2019 Dec; 54():110-116. PubMed ID: 31408805
[TBL] [Abstract][Full Text] [Related]
18. Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs.
McQuisten KA; Peek AS
PLoS One; 2009 Oct; 4(10):e7522. PubMed ID: 19847297
[TBL] [Abstract][Full Text] [Related]
19. A comparative study on feature selection for a risk prediction model for colorectal cancer.
Cueto-López N; García-Ordás MT; Dávila-Batista V; Moreno V; Aragonés N; Alaiz-Rodríguez R
Comput Methods Programs Biomed; 2019 Aug; 177():219-229. PubMed ID: 31319951
[TBL] [Abstract][Full Text] [Related]
20. Efficacy of head and neck computed tomography for skeletal muscle mass estimation in patients with head and neck cancer.
Jung AR; Roh JL; Kim JS; Choi SH; Nam SY; Kim SY
Oral Oncol; 2019 Aug; 95():95-99. PubMed ID: 31345401
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
