561 related articles for article (PubMed ID: 38172962)
1. 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]
2. Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases.
Peng S; Huang J; Liu X; Deng J; Sun C; Tang J; Chen H; Cao W; Wang W; Duan X; Luo X; Peng S
Front Cardiovasc Med; 2022; 9():994359. PubMed ID: 36312291
[TBL] [Abstract][Full Text] [Related]
3. A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.
Zhao QY; Liu LP; Luo JC; Luo YW; Wang H; Zhang YJ; Gui R; Tu GW; Luo Z
Front Med (Lausanne); 2020; 7():637434. PubMed ID: 33553224
[No Abstract] [Full Text] [Related]
4. Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database.
Lu Z; Zhang J; Hong J; Wu J; Liu Y; Xiao W; Hua T; Yang M
Front Med (Lausanne); 2021; 8():661710. PubMed ID: 33889591
[No Abstract] [Full Text] [Related]
5. Factor analysis based on SHapley Additive exPlanations for sepsis-associated encephalopathy in ICU mortality prediction using XGBoost - a retrospective study based on two large database.
Guo J; Cheng H; Wang Z; Qiao M; Li J; Lyu J
Front Neurol; 2023; 14():1290117. PubMed ID: 38162445
[TBL] [Abstract][Full Text] [Related]
6. A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit.
Zhuang J; Huang H; Jiang S; Liang J; Liu Y; Yu X
BMC Med Inform Decis Mak; 2023 Sep; 23(1):185. PubMed ID: 37715194
[TBL] [Abstract][Full Text] [Related]
7. Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers.
Zhang G; Shao F; Yuan W; Wu J; Qi X; Gao J; Shao R; Tang Z; Wang T
Eur J Med Res; 2024 Mar; 29(1):156. PubMed ID: 38448999
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.
Wen C; Zhang X; Li Y; Xiao W; Hu Q; Lei X; Xu T; Liang S; Gao X; Zhang C; Yu Z; Lü M
PLoS One; 2024; 19(5):e0303469. PubMed ID: 38768153
[TBL] [Abstract][Full Text] [Related]
11. [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]
12. 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]
13. Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study.
Li J; Liu S; Hu Y; Zhu L; Mao Y; Liu J
J Med Internet Res; 2022 Aug; 24(8):e38082. PubMed ID: 35943767
[TBL] [Abstract][Full Text] [Related]
14. Twenty-eight-day in-hospital mortality prediction for elderly patients with ischemic stroke in the intensive care unit: Interpretable machine learning models.
Huang J; Jin W; Duan X; Liu X; Shu T; Fu L; Deng J; Chen H; Liu G; Jiang Y; Liu Z
Front Public Health; 2022; 10():1086339. PubMed ID: 36711330
[TBL] [Abstract][Full Text] [Related]
15. Construction and validation of prognostic models in critically Ill patients with sepsis-associated acute kidney injury: interpretable machine learning approach.
Fan Z; Jiang J; Xiao C; Chen Y; Xia Q; Wang J; Fang M; Wu Z; Chen F
J Transl Med; 2023 Jun; 21(1):406. PubMed ID: 37349774
[TBL] [Abstract][Full Text] [Related]
16. Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation.
Pan P; Li Y; Xiao Y; Han B; Su L; Su M; Li Y; Zhang S; Jiang D; Chen X; Zhou F; Ma L; Bao P; Xie L
J Med Internet Res; 2020 Nov; 22(11):e23128. PubMed ID: 33035175
[TBL] [Abstract][Full Text] [Related]
17. A Simple Weaning Model Based on Interpretable Machine Learning Algorithm for Patients With Sepsis: A Research of MIMIC-IV and eICU Databases.
Liu W; Tao G; Zhang Y; Xiao W; Zhang J; Liu Y; Lu Z; Hua T; Yang M
Front Med (Lausanne); 2021; 8():814566. PubMed ID: 35118099
[TBL] [Abstract][Full Text] [Related]
18. Development of a machine learning-based prediction model for sepsis-associated delirium in the intensive care unit.
Zhang Y; Hu J; Hua T; Zhang J; Zhang Z; Yang M
Sci Rep; 2023 Aug; 13(1):12697. PubMed ID: 37542106
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Interpretable machine learning model for early prediction of delirium in elderly patients following intensive care unit admission: a derivation and validation study.
Tang D; Ma C; Xu Y
Front Med (Lausanne); 2024; 11():1399848. PubMed ID: 38828233
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
