634 related articles for article (PubMed ID: 37001474)
1. Machine learning prediction models and nomogram to predict the risk of in-hospital death for severe DKA: A clinical study based on MIMIC-IV, eICU databases, and a college hospital ICU.
Xie W; Li Y; Meng X; Zhao M
Int J Med Inform; 2023 Jun; 174():105049. PubMed ID: 37001474
[TBL] [Abstract][Full Text] [Related]
2. Nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-III database.
Fan T; Wang H; Wang J; Wang W; Guan H; Zhang C
BMC Endocr Disord; 2021 Mar; 21(1):37. PubMed ID: 33663489
[TBL] [Abstract][Full Text] [Related]
3. Clinical nomogram prediction model to assess the risk of prolonged ICU length of stay in patients with diabetic ketoacidosis: a retrospective analysis based on the MIMIC-IV database.
Shi J; Chen F; Zheng K; Su T; Wang X; Wu J; Ni B; Pan Y
BMC Anesthesiol; 2024 Feb; 24(1):86. PubMed ID: 38424557
[TBL] [Abstract][Full Text] [Related]
4. [Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].
Xie Z; Jin J; Liu D; Lu S; Yu H; Han D; Sun W; Huang M
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2024 Apr; 36(4):345-352. PubMed ID: 38813626
[TBL] [Abstract][Full Text] [Related]
5. Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury.
Xing Z; Cai L; Wu Y; Shen P; Fu X; Xu Y; Wang J
Eur J Med Res; 2024 Jan; 29(1):80. PubMed ID: 38287435
[TBL] [Abstract][Full Text] [Related]
6. Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning.
Sun Y; He Z; Ren J; Wu Y
BMC Anesthesiol; 2023 May; 23(1):178. PubMed ID: 37231340
[TBL] [Abstract][Full Text] [Related]
7. Development and Internal Validation of a Nomogram to Predict Mortality During the ICU Stay of Thoracic Fracture Patients Without Neurological Compromise: An Analysis of the MIMIC-III Clinical Database.
Wang H; Ou Y; Fan T; Zhao J; Kang M; Dong R; Qu Y
Front Public Health; 2021; 9():818439. PubMed ID: 35004604
[No Abstract] [Full Text] [Related]
8. Development and validation of a nomogram for predicting in-hospital mortality in patients with nonhip femoral fractures.
Xing Z; Xu Y; Wu Y; Fu X; Shen P; Che W; Wang J
Eur J Med Res; 2023 Nov; 28(1):539. PubMed ID: 38001553
[TBL] [Abstract][Full Text] [Related]
9. A simple APACHE IV risk dynamic nomogram that incorporates early admitted lactate for the initial assessment of 28-day mortality in critically ill patients with acute myocardial infarction.
Song J; Yu T; Yan Q; Wu L; Li S; Wang L
BMC Cardiovasc Disord; 2022 Nov; 22(1):502. PubMed ID: 36434509
[TBL] [Abstract][Full Text] [Related]
10. Nomogram predictive model for in-hospital mortality risk in elderly ICU patients with urosepsis.
Wei J; Liang R; Liu S; Dong W; Gao J; Hua T; Xiao W; Li H; Zhu H; Hu J; Cao S; Liu Y; Lyu J; Yang M
BMC Infect Dis; 2024 Apr; 24(1):442. PubMed ID: 38671376
[TBL] [Abstract][Full Text] [Related]
11. Development of a novel tool: a nomogram for predicting in-hospital mortality of patients in intensive care unit after percutaneous coronary intervention.
Yuan M; Ren BC; Wang Y; Ren F; Gao D
BMC Anesthesiol; 2023 Jan; 23(1):5. PubMed ID: 36609220
[TBL] [Abstract][Full Text] [Related]
12. Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost.
Fan T; Wang J; Li L; Kang J; Wang W; Zhang C
Front Public Health; 2023; 11():1087297. PubMed ID: 37089510
[TBL] [Abstract][Full Text] [Related]
13. Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database.
Li F; Xin H; Zhang J; Fu M; Zhou J; Lian Z
BMJ Open; 2021 Jul; 11(7):e044779. PubMed ID: 34301649
[TBL] [Abstract][Full Text] [Related]
14. Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit.
Qi Z; Dong L; Lin J; Duan M
Front Cell Infect Microbiol; 2024; 14():1348896. PubMed ID: 38500500
[TBL] [Abstract][Full Text] [Related]
15. [Establishment and evaluation of early in-hospital death prediction model for patients with acute pancreatitis in intensive care unit].
Yu L; Zhou X; Li Y; Liu M
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Aug; 35(8):865-869. PubMed ID: 37593868
[TBL] [Abstract][Full Text] [Related]
16. Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation.
Zhou S; Lu Z; Liu Y; Wang M; Zhou W; Cui X; Zhang J; Xiao W; Hua T; Zhu H; Yang M
Eur J Med Res; 2024 Jan; 29(1):14. PubMed ID: 38172962
[TBL] [Abstract][Full Text] [Related]
17. Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure.
Chen Z; Li T; Guo S; Zeng D; Wang K
Front Cardiovasc Med; 2023; 10():1119699. PubMed ID: 37077747
[TBL] [Abstract][Full Text] [Related]
18. Predictive nomogram for 28-day mortality risk in mitral valve disorder patients in the intensive care unit: A comprehensive assessment from the MIMIC-III database.
Qiu Y; Li M; Song X; Li Z; Ma A; Meng Z; Li Y; Tan M
Int J Cardiol; 2024 Jul; 407():132105. PubMed ID: 38677334
[TBL] [Abstract][Full Text] [Related]
19. Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit.
Huang T; Le D; Yuan L; Xu S; Peng X
PLoS One; 2023; 18(1):e0280606. PubMed ID: 36701342
[TBL] [Abstract][Full Text] [Related]
20. A machine learning-based prediction model for in-hospital mortality among critically ill patients with hip fracture: An internal and external validated study.
Lei M; Han Z; Wang S; Han T; Fang S; Lin F; Huang T
Injury; 2023 Feb; 54(2):636-644. PubMed ID: 36414503
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
