256 related articles for article (PubMed ID: 37685276)
1. Machine Learning Models for Prediction of Severe
Liu Y; Qiu T; Hu H; Kong C; Zhang Y; Wang T; Zhou J; Zou J
Diagnostics (Basel); 2023 Aug; 13(17):. PubMed ID: 37685276
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Prediction of Acute Kidney Injury after Extracorporeal Cardiac Surgery (CSA-AKI) by Machine Learning Algorithms.
Tong Y; Niu X; Liu F
Heart Surg Forum; 2023 Oct; 26(5):E537-E551. PubMed ID: 37920093
[TBL] [Abstract][Full Text] [Related]
4. The Development and Evaluation of a Prediction Model for Kidney Transplant-Based
Zhang L; Liu Y; Zou J; Wang T; Hu H; Zhou Y; Lu Y; Qiu T; Zhou J; Liu X
Biomedicines; 2024 Feb; 12(2):. PubMed ID: 38397968
[TBL] [Abstract][Full Text] [Related]
5. Application of machine learning techniques in real-world research to predict the risk of liver metastasis in rectal cancer.
Qiu B; Su XH; Qin X; Wang Q
Front Oncol; 2022; 12():1065468. PubMed ID: 36605425
[TBL] [Abstract][Full Text] [Related]
6. Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage.
Geng Z; Yang C; Zhao Z; Yan Y; Guo T; Liu C; Wu A; Wu X; Wei L; Tian Y; Hu P; Wang K
J Transl Med; 2024 Mar; 22(1):236. PubMed ID: 38439097
[TBL] [Abstract][Full Text] [Related]
7. Performance Analysis of Conventional Machine Learning Algorithms for Identification of Chronic Kidney Disease in Type 1 Diabetes Mellitus Patients.
Chowdhury NH; Reaz MBI; Haque F; Ahmad S; Ali SHM; A Bakar AA; Bhuiyan MAS
Diagnostics (Basel); 2021 Dec; 11(12):. PubMed ID: 34943504
[TBL] [Abstract][Full Text] [Related]
8. A machine learning-based prediction model pre-operatively for functional recovery after 1-year of hip fracture surgery in older people.
Lin C; Liang Z; Liu J; Sun W
Front Surg; 2023; 10():1160085. PubMed ID: 37351328
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Interpretable machine learning for predicting 28-day all-cause in-hospital mortality for hypertensive ischemic or hemorrhagic stroke patients in the ICU: a multi-center retrospective cohort study with internal and external cross-validation.
Huang J; Chen H; Deng J; Liu X; Shu T; Yin C; Duan M; Fu L; Wang K; Zeng S
Front Neurol; 2023; 14():1185447. PubMed ID: 37614971
[TBL] [Abstract][Full Text] [Related]
11. Machine learning models for prediction of invasion Klebsiella pneumoniae liver abscess syndrome in diabetes mellitus: a singled centered retrospective study.
Feng C; Di J; Jiang S; Li X; Hua F
BMC Infect Dis; 2023 May; 23(1):284. PubMed ID: 37142976
[TBL] [Abstract][Full Text] [Related]
12. Machine learning-based prediction of cerebral hemorrhage in patients with hemodialysis: A multicenter, retrospective study.
Li F; Chen A; Li Z; Gu L; Pan Q; Wang P; Fan Y; Feng J
Front Neurol; 2023; 14():1139096. PubMed ID: 37077571
[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. [Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning].
Zhu M; Hu C; He Y; Qian Y; Tang S; Hu Q; Hao C
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Jul; 35(7):696-701. PubMed ID: 37545445
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. 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]
18. Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach.
Lee CL; Liu WJ; Tsai SF
Nutrients; 2022 Jul; 14(14):. PubMed ID: 35889789
[TBL] [Abstract][Full Text] [Related]
19. Ten-Year Multicenter Retrospective Study Utilizing Machine Learning Algorithms to Identify Patients at High Risk of Venous Thromboembolism After Radical Gastrectomy.
Liu Y; Song C; Tian Z; Shen W
Int J Gen Med; 2023; 16():1909-1925. PubMed ID: 37228741
[TBL] [Abstract][Full Text] [Related]
20. Application of interpretable machine learning algorithms to predict distant metastasis in osteosarcoma.
Bai BL; Wu ZY; Weng SJ; Yang Q
Cancer Med; 2023 Feb; 12(4):5025-5034. PubMed ID: 36082478
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
