660 related articles for article (PubMed ID: 31346948)
1. Identification of clinically relevant features in hypertensive patients using penalized regression: a case study of cardiovascular events.
Garcia-Carretero R; Barquero-Perez O; Mora-Jimenez I; Soguero-Ruiz C; Goya-Esteban R; Ramos-Lopez J
Med Biol Eng Comput; 2019 Sep; 57(9):2011-2026. PubMed ID: 31346948
[TBL] [Abstract][Full Text] [Related]
2. Logistic LASSO and Elastic Net to Characterize Vitamin D Deficiency in a Hypertensive Obese Population.
Garcia-Carretero R; Vigil-Medina L; Barquero-Perez O; Mora-Jimenez I; Soguero-Ruiz C; Goya-Esteban R; Ramos-Lopez J
Metab Syndr Relat Disord; 2020 Mar; 18(2):79-85. PubMed ID: 31928513
[No Abstract] [Full Text] [Related]
3. Comparison of Cox Model Methods in A Low-dimensional Setting with Few Events.
Ojeda FM; Müller C; Börnigen D; Trégouët DA; Schillert A; Heinig M; Zeller T; Schnabel RB
Genomics Proteomics Bioinformatics; 2016 Aug; 14(4):235-43. PubMed ID: 27224515
[TBL] [Abstract][Full Text] [Related]
4. Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.
Jovanovic M; Radovanovic S; Vukicevic M; Van Poucke S; Delibasic B
Artif Intell Med; 2016 Sep; 72():12-21. PubMed ID: 27664505
[TBL] [Abstract][Full Text] [Related]
5. A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population.
Chowdhury MZI; Leung AA; Walker RL; Sikdar KC; O'Beirne M; Quan H; Turin TC
Sci Rep; 2023 Jan; 13(1):13. PubMed ID: 36593280
[TBL] [Abstract][Full Text] [Related]
6. High-dimensional Cox models: the choice of penalty as part of the model building process.
Benner A; Zucknick M; Hielscher T; Ittrich C; Mansmann U
Biom J; 2010 Feb; 52(1):50-69. PubMed ID: 20166132
[TBL] [Abstract][Full Text] [Related]
7. Robust estimation of the expected survival probabilities from high-dimensional Cox models with biomarker-by-treatment interactions in randomized clinical trials.
Ternès N; Rotolo F; Michiels S
BMC Med Res Methodol; 2017 May; 17(1):83. PubMed ID: 28532387
[TBL] [Abstract][Full Text] [Related]
8. Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease.
Zhu H; Qiao S; Zhao D; Wang K; Wang B; Niu Y; Shang S; Dong Z; Zhang W; Zheng Y; Chen X
Front Endocrinol (Lausanne); 2024; 15():1390729. PubMed ID: 38863928
[TBL] [Abstract][Full Text] [Related]
9. Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data.
Gui J; Li H
Bioinformatics; 2005 Jul; 21(13):3001-8. PubMed ID: 15814556
[TBL] [Abstract][Full Text] [Related]
10. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.
Alaa AM; Bolton T; Di Angelantonio E; Rudd JHF; van der Schaar M
PLoS One; 2019; 14(5):e0213653. PubMed ID: 31091238
[TBL] [Abstract][Full Text] [Related]
11. Prediction Model of Cardiac Risk for Dental Extraction in Elderly Patients with Cardiovascular Diseases.
Tang M; Hu P; Wang CF; Yu CQ; Sheng J; Ma SJ
Gerontology; 2019; 65(6):591-598. PubMed ID: 31048587
[TBL] [Abstract][Full Text] [Related]
12. Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions.
Ogutu JO; Schulz-Streeck T; Piepho HP
BMC Proc; 2012 May; 6 Suppl 2(Suppl 2):S10. PubMed ID: 22640436
[TBL] [Abstract][Full Text] [Related]
13. Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.
Corey KM; Kashyap S; Lorenzi E; Lagoo-Deenadayalan SA; Heller K; Whalen K; Balu S; Heflin MT; McDonald SR; Swaminathan M; Sendak M
PLoS Med; 2018 Nov; 15(11):e1002701. PubMed ID: 30481172
[TBL] [Abstract][Full Text] [Related]
14. Comparison of Penalized Cox Regression Methods in Low-Dimensional Data with Few-Events: An Application to Dialysis Patients' Data.
Rafati S; Baneshi MR; Hassani L; Bahrampour A
J Res Health Sci; 2019 Jul; 19(3):e00452. PubMed ID: 31586373
[TBL] [Abstract][Full Text] [Related]
15. Derivation and internal validation of an expanded cardiovascular risk prediction score for rheumatoid arthritis: a Consortium of Rheumatology Researchers of North America Registry Study.
Solomon DH; Greenberg J; Curtis JR; Liu M; Farkouh ME; Tsao P; Kremer JM; Etzel CJ
Arthritis Rheumatol; 2015 May; 67(8):1995-2003. PubMed ID: 25989470
[TBL] [Abstract][Full Text] [Related]
16. Predicting Occurrence of Spine Surgery Complications Using "Big Data" Modeling of an Administrative Claims Database.
Ratliff JK; Balise R; Veeravagu A; Cole TS; Cheng I; Olshen RA; Tian L
J Bone Joint Surg Am; 2016 May; 98(10):824-34. PubMed ID: 27194492
[TBL] [Abstract][Full Text] [Related]
17. Prediction model for cardiovascular events or all-cause mortality in incident dialysis patients.
Inaguma D; Morii D; Kabata D; Yoshida H; Tanaka A; Koshi-Ito E; Takahashi K; Hayashi H; Koide S; Tsuboi N; Hasegawa M; Shintani A; Yuzawa Y
PLoS One; 2019; 14(8):e0221352. PubMed ID: 31437231
[TBL] [Abstract][Full Text] [Related]
18. A machine learning approach to predict early outcomes after pituitary adenoma surgery.
Hollon TC; Parikh A; Pandian B; Tarpeh J; Orringer DA; Barkan AL; McKean EL; Sullivan SE
Neurosurg Focus; 2018 Nov; 45(5):E8. PubMed ID: 30453460
[TBL] [Abstract][Full Text] [Related]
19. 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]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]