173 related articles for article (PubMed ID: 37296269)
1. A simulation study on missing data imputation for dichotomous variables using statistical and machine learning methods.
Ge Y; Li Z; Zhang J
Sci Rep; 2023 Jun; 13(1):9432. PubMed ID: 37296269
[TBL] [Abstract][Full Text] [Related]
2. Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets.
Li J; Guo S; Ma R; He J; Zhang X; Rui D; Ding Y; Li Y; Jian L; Cheng J; Guo H
BMC Med Res Methodol; 2024 Feb; 24(1):41. PubMed ID: 38365610
[TBL] [Abstract][Full Text] [Related]
3. Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example.
Wang H; Tang J; Wu M; Wang X; Zhang T
BMC Med Inform Decis Mak; 2022 Jan; 22(1):13. PubMed ID: 35027065
[TBL] [Abstract][Full Text] [Related]
4. The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model.
Guo CY; Yang YC; Chen YH
Front Public Health; 2021; 9():680054. PubMed ID: 34291028
[TBL] [Abstract][Full Text] [Related]
5. Combining data discretization and missing value imputation for incomplete medical datasets.
Huang MW; Tsai CF; Tsui SC; Lin WC
PLoS One; 2023; 18(11):e0295032. PubMed ID: 38033140
[TBL] [Abstract][Full Text] [Related]
6. Missing value imputation in high-dimensional phenomic data: imputable or not, and how?
Liao SG; Lin Y; Kang DD; Chandra D; Bon J; Kaminski N; Sciurba FC; Tseng GC
BMC Bioinformatics; 2014 Nov; 15(1):346. PubMed ID: 25371041
[TBL] [Abstract][Full Text] [Related]
7. A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies.
Khondoker M; Dobson R; Skirrow C; Simmons A; Stahl D
Stat Methods Med Res; 2016 Oct; 25(5):1804-1823. PubMed ID: 24047600
[TBL] [Abstract][Full Text] [Related]
8. Evaluation of machine learning methods for covariate data imputation in pharmacometrics.
Bräm DS; Nahum U; Atkinson A; Koch G; Pfister M
CPT Pharmacometrics Syst Pharmacol; 2022 Dec; 11(12):1638-1648. PubMed ID: 36346135
[TBL] [Abstract][Full Text] [Related]
9. Advanced methods for missing values imputation based on similarity learning.
Fouad KM; Ismail MM; Azar AT; Arafa MM
PeerJ Comput Sci; 2021; 7():e619. PubMed ID: 34395861
[TBL] [Abstract][Full Text] [Related]
10. Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage.
Tang J; Wang X; Wan H; Lin C; Shao Z; Chang Y; Wang H; Wu Y; Zhang T; Du Y
BMC Med Inform Decis Mak; 2022 Oct; 22(1):278. PubMed ID: 36284327
[TBL] [Abstract][Full Text] [Related]
11. Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data.
Kasaraneni PP; Venkata Pavan Kumar Y; Moganti GLK; Kannan R
Sensors (Basel); 2022 Nov; 22(23):. PubMed ID: 36502025
[TBL] [Abstract][Full Text] [Related]
12. Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation.
Mikhchi A; Honarvar M; Kashan NE; Aminafshar M
J Theor Biol; 2016 Jun; 399():148-58. PubMed ID: 27049046
[TBL] [Abstract][Full Text] [Related]
13. Generative adversarial networks for imputing missing data for big data clinical research.
Dong W; Fong DYT; Yoon JS; Wan EYF; Bedford LE; Tang EHM; Lam CLK
BMC Med Res Methodol; 2021 Apr; 21(1):78. PubMed ID: 33879090
[TBL] [Abstract][Full Text] [Related]
14. A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis.
Jahangiri M; Kazemnejad A; Goldfeld KS; Daneshpour MS; Mostafaei S; Khalili D; Moghadas MR; Akbarzadeh M
BMC Med Res Methodol; 2023 Jul; 23(1):161. PubMed ID: 37415114
[TBL] [Abstract][Full Text] [Related]
15. Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women.
Shim JG; Kim DW; Ryu KH; Cho EA; Ahn JH; Kim JI; Lee SH
Arch Osteoporos; 2020 Oct; 15(1):169. PubMed ID: 33097976
[TBL] [Abstract][Full Text] [Related]
16. On mining incomplete medical datasets: Ordering imputation and classification.
Chen CW; Lin WC; Ke SW; Tsai CF; Hu YH
Technol Health Care; 2015; 23(5):619-25. PubMed ID: 26410122
[TBL] [Abstract][Full Text] [Related]
17. Self-Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study.
Gwon H; Ahn I; Kim Y; Kang HJ; Seo H; Cho HN; Choi H; Jun TJ; Kim YH
JMIR Public Health Surveill; 2021 Oct; 7(10):e30824. PubMed ID: 34643539
[TBL] [Abstract][Full Text] [Related]
18. Missing data techniques in classification for cardiovascular dysautonomias diagnosis.
Idri A; Kadi I; Abnane I; Fernandez-Aleman JL
Med Biol Eng Comput; 2020 Nov; 58(11):2863-2878. PubMed ID: 32970269
[TBL] [Abstract][Full Text] [Related]
19. Comparison of imputation methods for missing production data of dairy cattle.
You J; Ellis JL; Adams S; Sahar M; Jacobs M; Tulpan D
Animal; 2023 Dec; 17 Suppl 5():100921. PubMed ID: 37659911
[TBL] [Abstract][Full Text] [Related]
20. Imputation of missing values in lipidomic datasets.
Frölich N; Klose C; Widén E; Ripatti S; Gerl MJ
Proteomics; 2024 Apr; ():e2300606. PubMed ID: 38602226
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]