139 related articles for article (PubMed ID: 38754284)
1. Risk prediction model of metabolic syndrome in perimenopausal women based on machine learning.
Xiaoxue W; Zijun W; Shichen C; Mukun Y; Yi C; Linqing M; Wenpei B
Int J Med Inform; 2024 Aug; 188():105480. PubMed ID: 38754284
[TBL] [Abstract][Full Text] [Related]
2. Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea.
Kim J; Mun S; Lee S; Jeong K; Baek Y
BMC Public Health; 2022 Apr; 22(1):664. PubMed ID: 35387629
[TBL] [Abstract][Full Text] [Related]
3. Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery.
Chen X; Pan J; Li Y; Tang R
Aging Clin Exp Res; 2023 Nov; 35(11):2643-2656. PubMed ID: 37733228
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.
Liu L; Bi B; Cao L; Gui M; Ju F
Front Endocrinol (Lausanne); 2024; 15():1320335. PubMed ID: 38481447
[TBL] [Abstract][Full Text] [Related]
6. Establishment and validation of a heart failure risk prediction model for elderly patients after coronary rotational atherectomy based on machine learning.
Zhang L; Zhou X; Cao J
PeerJ; 2024; 12():e16867. PubMed ID: 38313005
[TBL] [Abstract][Full Text] [Related]
7. Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults.
Shi Y; Zhang Y; Cao Z; Ma L; Yuan Y; Niu X; Su Y; Xie Y; Chen X; Xing L; Hei X; Liu H; Wu S; Li W; Ren X
BMC Med Inform Decis Mak; 2023 Oct; 23(1):230. PubMed ID: 37858225
[TBL] [Abstract][Full Text] [Related]
8. Prediction of the development of acute kidney injury following cardiac surgery by machine learning.
Tseng PY; Chen YT; Wang CH; Chiu KM; Peng YS; Hsu SP; Chen KL; Yang CY; Lee OK
Crit Care; 2020 Jul; 24(1):478. PubMed ID: 32736589
[TBL] [Abstract][Full Text] [Related]
9. A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP.
Zhang G; Shi Y; Yin P; Liu F; Fang Y; Li X; Zhang Q; Zhang Z
Front Oncol; 2022; 12():944569. PubMed ID: 35957890
[TBL] [Abstract][Full Text] [Related]
10. [Comparison of machine learning and Logistic regression model in predicting acute kidney injury after cardiac surgery: data analysis based on MIMIC-III database].
Xiong W; Zhang L; She K; Xu G; Bai S; Liu X
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Nov; 34(11):1188-1193. PubMed ID: 36567564
[TBL] [Abstract][Full Text] [Related]
11. An interpretable machine learning model to predict off-pump coronary artery bypass grafting-associated acute kidney injury.
Zeng Z; Tian X; Li L; Diao Y; Zhang T
Adv Clin Exp Med; 2024 May; 33(5):473-481. PubMed ID: 37593773
[TBL] [Abstract][Full Text] [Related]
12. Machine learning-based models for the prediction of breast cancer recurrence risk.
Zuo D; Yang L; Jin Y; Qi H; Liu Y; Ren L
BMC Med Inform Decis Mak; 2023 Nov; 23(1):276. PubMed ID: 38031071
[TBL] [Abstract][Full Text] [Related]
13. Interpretable machine learning model to predict surgical difficulty in laparoscopic resection for rectal cancer.
Yu M; Yuan Z; Li R; Shi B; Wan D; Dong X
Front Oncol; 2024; 14():1337219. PubMed ID: 38380369
[TBL] [Abstract][Full Text] [Related]
14. Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort.
Zhou R; Li Z; Liu J; Qian D; Meng X; Guan L; Sun X; Li H; Yu M
Front Cardiovasc Med; 2024; 11():1344170. PubMed ID: 38486703
[TBL] [Abstract][Full Text] [Related]
15. Supervised learning applied to classifying fallers versus non-fallers among older adults with cancer.
Ramsdale E; Kunduru M; Smith L; Culakova E; Shen J; Meng S; Zand M; Anand A
J Geriatr Oncol; 2023 May; 14(4):101498. PubMed ID: 37084629
[TBL] [Abstract][Full Text] [Related]
16. Develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy.
Zou XC; Luo CW; Yuan RM; Jin MN; Zeng T; Chao HC
Urolithiasis; 2024 Apr; 52(1):64. PubMed ID: 38613668
[TBL] [Abstract][Full Text] [Related]
17. Using Machine Learning Approaches to Predict Short-Term Risk of Cardiotoxicity Among Patients with Colorectal Cancer After Starting Fluoropyrimidine-Based Chemotherapy.
Li C; Chen L; Chou C; Ngorsuraches S; Qian J
Cardiovasc Toxicol; 2022 Feb; 22(2):130-140. PubMed ID: 34792740
[TBL] [Abstract][Full Text] [Related]
18. Machine learning-based identification of symptomatic carotid atherosclerotic plaques with dual-energy computed tomography angiography.
Wang LJ; Zhai PQ; Xue LL; Shi CY; Zhang Q; Zhang H
J Stroke Cerebrovasc Dis; 2023 Aug; 32(8):107209. PubMed ID: 37290153
[TBL] [Abstract][Full Text] [Related]
19. Application of an Interpretable Machine Learning Model to Predict Lymph Node Metastasis in Patients with Laryngeal Carcinoma.
Feng M; Zhang J; Zhou X; Mo H; Jia L; Zhang C; Hu Y; Yuan W
J Oncol; 2022; 2022():6356399. PubMed ID: 36411795
[TBL] [Abstract][Full Text] [Related]
20. Using machine learning to identify patients at high risk of developing low bone density or osteoporosis after gastrectomy: a 10-year multicenter retrospective analysis.
Zhu Y; Liu Y; Wang Q; Niu S; Wang L; Cheng C; Chen X; Liu J; Zhao S
J Cancer Res Clin Oncol; 2023 Dec; 149(19):17479-17493. PubMed ID: 37897658
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
