211 related articles for article (PubMed ID: 38150124)
1. 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]
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. Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study.
Li M; Han S; Liang F; Hu C; Zhang B; Hou Q; Zhao S
J Med Internet Res; 2024 May; 26():e51354. PubMed ID: 38691403
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
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. 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]
8. 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]
9. 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]
10. 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]
11. Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury.
Gao T; Nong Z; Luo Y; Mo M; Chen Z; Yang Z; Pan L
Ren Fail; 2024 Dec; 46(1):2316267. PubMed ID: 38369749
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. 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]
15. 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]
16. 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]
17. Machine learning algorithm to predict mortality in critically ill patients with sepsis-associated acute kidney injury.
Li X; Wu R; Zhao W; Shi R; Zhu Y; Wang Z; Pan H; Wang D
Sci Rep; 2023 Mar; 13(1):5223. PubMed ID: 36997585
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan.
Chan MC; Pai KC; Su SA; Wang MS; Wu CL; Chao WC
BMC Med Inform Decis Mak; 2022 Mar; 22(1):75. PubMed ID: 35337303
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]