154 related articles for article (PubMed ID: 37422210)
1. Effects of heavy metal exposure on hypertension: A machine learning modeling approach.
Li W; Huang G; Tang N; Lu P; Jiang L; Lv J; Qin Y; Lin Y; Xu F; Lei D
Chemosphere; 2023 Oct; 337():139435. PubMed ID: 37422210
[TBL] [Abstract][Full Text] [Related]
2. Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective.
Liu J; Li X; Zhu P
Biol Trace Elem Res; 2024 Feb; ():. PubMed ID: 38409445
[TBL] [Abstract][Full Text] [Related]
3. Exploring the association of metal mixture in blood to the kidney function and tumor necrosis factor alpha using machine learning methods.
Luo KH; Wu CH; Yang CC; Chen TH; Tu HP; Yang CH; Chuang HY
Ecotoxicol Environ Saf; 2023 Oct; 265():115528. PubMed ID: 37783110
[TBL] [Abstract][Full Text] [Related]
4. Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach.
Gui Y; Gui S; Wang X; Li Y; Xu Y; Zhang J
Sci Rep; 2024 Jun; 14(1):13049. PubMed ID: 38844504
[TBL] [Abstract][Full Text] [Related]
5. The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method.
Xu S; Sun M
J Clin Hypertens (Greenwich); 2024 Feb; 26(2):187-196. PubMed ID: 38214193
[TBL] [Abstract][Full Text] [Related]
6. A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study.
Wang J; Chen H; Wang H; Liu W; Peng D; Zhao Q; Xiao M
J Med Internet Res; 2023 Apr; 25():e43815. PubMed ID: 37023416
[TBL] [Abstract][Full Text] [Related]
7. The relationship between heavy metals and metabolic syndrome using machine learning.
Yao J; Du Z; Yang F; Duan R; Feng T
Front Public Health; 2024; 12():1378041. PubMed ID: 38686033
[TBL] [Abstract][Full Text] [Related]
8. Development of an interpretable machine learning model associated with heavy metals' exposure to identify coronary heart disease among US adults via SHAP: Findings of the US NHANES from 2003 to 2018.
Li X; Zhao Y; Zhang D; Kuang L; Huang H; Chen W; Fu X; Wu Y; Li T; Zhang J; Yuan L; Hu H; Liu Y; Zhang M; Hu F; Sun X; Hu D
Chemosphere; 2023 Jan; 311(Pt 1):137039. PubMed ID: 36342026
[TBL] [Abstract][Full Text] [Related]
9. Application of interpretable machine learning algorithms to predict distant metastasis in ovarian clear cell carcinoma.
Guo QH; Xie FC; Zhong FM; Wen W; Zhang XR; Yu XJ; Wang XL; Huang B; Li LP; Wang XZ
Cancer Med; 2024 Apr; 13(7):e7161. PubMed ID: 38613173
[TBL] [Abstract][Full Text] [Related]
10. Building a predictive model for hypertension related to environmental chemicals using machine learning.
Liu S; Lu L; Wang F; Han B; Ou L; Gao X; Luo Y; Huo W; Zeng Q
Environ Sci Pollut Res Int; 2024 Jan; 31(3):4595-4605. PubMed ID: 38105323
[TBL] [Abstract][Full Text] [Related]
11. Development and validation of explainable machine-learning models for carotid atherosclerosis early screening.
Yun K; He T; Zhen S; Quan M; Yang X; Man D; Zhang S; Wang W; Han X
J Transl Med; 2023 May; 21(1):353. PubMed ID: 37246225
[TBL] [Abstract][Full Text] [Related]
12. Application of interpretable machine learning algorithms to predict acute kidney injury in patients with cerebral infarction in ICU.
Lu X; Chen Y; Zhang G; Zeng X; Lai L; Qu C
J Stroke Cerebrovasc Dis; 2024 Jul; 33(7):107729. PubMed ID: 38657830
[TBL] [Abstract][Full Text] [Related]
13. The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models.
Ye Z; An S; Gao Y; Xie E; Zhao X; Guo Z; Li Y; Shen N; Ren J; Zheng J
Eur J Med Res; 2023 Jan; 28(1):33. PubMed ID: 36653875
[TBL] [Abstract][Full Text] [Related]
14. Prediction model of obstructive sleep apnea-related hypertension: Machine learning-based development and interpretation study.
Shi Y; Ma L; Chen X; Li W; Feng Y; Zhang Y; Cao Z; Yuan Y; Xie Y; Liu H; Yin L; Zhao C; Wu S; Ren X
Front Cardiovasc Med; 2022; 9():1042996. PubMed ID: 36545020
[TBL] [Abstract][Full Text] [Related]
15. Application of heavy metal immobilization in soil by biochar using machine learning.
Guo G; Lin L; Jin F; MaĊĦek O; Huang Q
Environ Res; 2023 Aug; 231(Pt 2):116098. PubMed ID: 37172676
[TBL] [Abstract][Full Text] [Related]
16. Machine learning-based models to predict the conversion of normal blood pressure to hypertension within 5-year follow-up.
Andishgar A; Bazmi S; Tabrizi R; Rismani M; Keshavarzian O; Pezeshki B; Ahmadizar F
PLoS One; 2024; 19(3):e0300201. PubMed ID: 38483860
[TBL] [Abstract][Full Text] [Related]
17. Identifying metabolic dysfunction-associated steatotic liver disease in patients with hypertension and pre-hypertension: An interpretable machine learning approach.
Chen C; Zhang W; Yan G; Tang C
Digit Health; 2024; 10():20552076241233135. PubMed ID: 38389508
[TBL] [Abstract][Full Text] [Related]
18. Application of machine learning techniques for predicting survival in ovarian cancer.
Sorayaie Azar A; Babaei Rikan S; Naemi A; Bagherzadeh Mohasefi J; Pirnejad H; Bagherzadeh Mohasefi M; Wiil UK
BMC Med Inform Decis Mak; 2022 Dec; 22(1):345. PubMed ID: 36585641
[TBL] [Abstract][Full Text] [Related]
19. Machine learning algorithms identify hypokalaemia risk in people with hypertension in the United States National Health and Nutrition Examination Survey 1999-2018.
Lin Z; Cheng YT; Cheung BMY
Ann Med; 2023 Dec; 55(1):2209336. PubMed ID: 37162442
[TBL] [Abstract][Full Text] [Related]
20. Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.
Shirwaikar RD; Acharya U D; Makkithaya K; M S; Srivastava S; Lewis U LES
Artif Intell Med; 2019 Jul; 98():59-76. PubMed ID: 31521253
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]