127 related articles for article (PubMed ID: 38010031)
1. MACHINE LEARNING FOR PREDICTING HEMODYNAMIC DETERIORATION OF PATIENTS WITH INTERMEDIATE-RISK PULMONARY EMBOLISM IN INTENSIVE CARE UNIT.
Xu J; Hu Z; Miao J; Cao L; Tian Z; Yao C; Huang K
Shock; 2024 Jan; 61(1):68-75. PubMed ID: 38010031
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease.
Huang D; Gong L; Wei C; Wang X; Liang Z
Respir Res; 2024 Jun; 25(1):246. PubMed ID: 38890628
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism.
Wang G; Xu J; Lin X; Lai W; Lv L; Peng S; Li K; Luo M; Chen J; Zhu D; Chen X; Yao C; Wu S; Huang K
BMC Cardiovasc Disord; 2023 Aug; 23(1):385. PubMed ID: 37533004
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. 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]
10. 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]
11. [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]
12. Predicting Hospital Survival in Patients Admitted to ICU with Pulmonary Embolism.
Ryll MJ; Zodl A; Weingarten TN; Rabinstein AA; Warner DO; Schroeder DR; Sprung J
J Intensive Care Med; 2024 May; 39(5):455-464. PubMed ID: 37964551
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. 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]
16. 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]
17. Use of the National Early Warning Score for predicting deterioration of patients with acute pulmonary embolism: a post-hoc analysis of the YEARS Study.
Bavalia R; Stals MAM; Mulder FI; Bistervels IM; Coppens M; Faber LM; Hendriks SV; Hofstee HMA; Huisman MV; van der Hulle T; Mairuhu ATA; Kruip MJHA; Middeldorp S; Klok FA; Hutten BA; Holleman F
Emerg Med J; 2023 Jan; 40(1):61-66. PubMed ID: 36344240
[TBL] [Abstract][Full Text] [Related]
18. Explainable machine learning model to predict refeeding hypophosphatemia.
Choi TY; Chang MY; Heo S; Jang JY
Clin Nutr ESPEN; 2021 Oct; 45():213-219. PubMed ID: 34620320
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]
