339 related articles for article (PubMed ID: 36581881)
1. Transferability and interpretability of the sepsis prediction models in the intensive care unit.
Chen Q; Li R; Lin C; Lai C; Chen D; Qu H; Huang Y; Lu W; Tang Y; Li L
BMC Med Inform Decis Mak; 2022 Dec; 22(1):343. PubMed ID: 36581881
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Interpretable machine-learning model for real-time, clustered risk factor analysis of sepsis and septic death in critical care.
Jiang Z; Bo L; Wang L; Xie Y; Cao J; Yao Y; Lu W; Deng X; Yang T; Bian J
Comput Methods Programs Biomed; 2023 Nov; 241():107772. PubMed ID: 37657148
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.
Kong G; Lin K; Hu Y
BMC Med Inform Decis Mak; 2020 Oct; 20(1):251. PubMed ID: 33008381
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning.
Cheng YW; Kuo PC; Chen SH; Kuo YT; Liu TL; Chan WS; Chan KC; Yeh YC
J Clin Monit Comput; 2024 Apr; 38(2):271-279. PubMed ID: 38150124
[TBL] [Abstract][Full Text] [Related]
9. Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study.
Hu C; Li L; Huang W; Wu T; Xu Q; Liu J; Hu B
Infect Dis Ther; 2022 Jun; 11(3):1117-1132. PubMed ID: 35399146
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records.
Thorsen-Meyer HC; Nielsen AB; Nielsen AP; Kaas-Hansen BS; Toft P; Schierbeck J; Strøm T; Chmura PJ; Heimann M; Dybdahl L; Spangsege L; Hulsen P; Belling K; Brunak S; Perner A
Lancet Digit Health; 2020 Apr; 2(4):e179-e191. PubMed ID: 33328078
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury.
Luo XQ; Yan P; Duan SB; Kang YX; Deng YH; Liu Q; Wu T; Wu X
Front Med (Lausanne); 2022; 9():853102. PubMed ID: 35783603
[TBL] [Abstract][Full Text] [Related]
16. Interpretable Machine Learning to Optimize Early In-Hospital Mortality Prediction for Elderly Patients with Sepsis: A Discovery Study.
Ke X; Zhang F; Huang G; Wang A
Comput Math Methods Med; 2022; 2022():4820464. PubMed ID: 36570336
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. 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]
20. Prediction of 30-day mortality in heart failure patients with hypoxic hepatitis: Development and external validation of an interpretable machine learning model.
Sun R; Wang X; Jiang H; Yan Y; Dong Y; Yan W; Luo X; Miu H; Qi L; Huang Z
Front Cardiovasc Med; 2022; 9():1035675. PubMed ID: 36386374
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]