516 related articles for article (PubMed ID: 36701342)
1. 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]
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. 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]
4. 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]
5. Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer.
Guo C; Pan J; Tian S; Gao Y
J Int Med Res; 2023 Nov; 51(11):3000605231198725. PubMed ID: 37950672
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. A time-incorporated SOFA score-based machine learning model for predicting mortality in critically ill patients: A multicenter, real-world study.
Liu Y; Gao K; Deng H; Ling T; Lin J; Yu X; Bo X; Zhou J; Gao L; Wang P; Hu J; Zhang J; Tong Z; Liu Y; Shi Y; Ke L; Gao Y; Li W
Int J Med Inform; 2022 Jul; 163():104776. PubMed ID: 35512625
[TBL] [Abstract][Full Text] [Related]
8. A Novel Composite Indicator of Predicting Mortality Risk for Heart Failure Patients With Diabetes Admitted to Intensive Care Unit Based on Machine Learning.
Yang B; Zhu Y; Lu X; Shen C
Front Endocrinol (Lausanne); 2022; 13():917838. PubMed ID: 35846312
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. 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]
12. 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]
13. Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation.
Liu X; Hu P; Yeung W; Zhang Z; Ho V; Liu C; Dumontier C; Thoral PJ; Mao Z; Cao D; Mark RG; Zhang Z; Feng M; Li D; Celi LA
Lancet Digit Health; 2023 Oct; 5(10):e657-e667. PubMed ID: 37599147
[TBL] [Abstract][Full Text] [Related]
14. Prediction of Mortality and Major Adverse Kidney Events in Critically Ill Patients With Acute Kidney Injury.
Neyra JA; Ortiz-Soriano V; Liu LJ; Smith TD; Li X; Xie D; Adams-Huet B; Moe OW; Toto RD; Chen J
Am J Kidney Dis; 2023 Jan; 81(1):36-47. PubMed ID: 35868537
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. [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]
17. 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]
18. Interpretable Machine Learning Model for Early Prediction of Mortality in ICU Patients with Rhabdomyolysis.
Liu C; Liu X; Mao Z; Hu P; Li X; Hu J; Hong Q; Geng X; Chi K; Zhou F; Cai G; Chen X; Sun X
Med Sci Sports Exerc; 2021 Sep; 53(9):1826-1834. PubMed ID: 33787533
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Using a machine learning approach to predict mortality in critically ill influenza patients: a cross-sectional retrospective multicentre study in Taiwan.
Hu CA; Chen CM; Fang YC; Liang SJ; Wang HC; Fang WF; Sheu CC; Perng WC; Yang KY; Kao KC; Wu CL; Tsai CS; Lin MY; Chao WC;
BMJ Open; 2020 Feb; 10(2):e033898. PubMed ID: 32102816
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
