301 related articles for article (PubMed ID: 30268673)
1. Artificial neural network algorithm model as powerful tool to predict acute lung injury following to severe acute pancreatitis.
Fei Y; Gao K; Li WQ
Pancreatology; 2018 Dec; 18(8):892-899. PubMed ID: 30268673
[TBL] [Abstract][Full Text] [Related]
2. Prediction and evaluation of the severity of acute respiratory distress syndrome following severe acute pancreatitis using an artificial neural network algorithm model.
Fei Y; Gao K; Li WQ
HPB (Oxford); 2019 Jul; 21(7):891-897. PubMed ID: 30591306
[TBL] [Abstract][Full Text] [Related]
3. Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.
Fei Y; Hu J; Li WQ; Wang W; Zong GQ
J Thromb Haemost; 2017 Mar; 15(3):439-445. PubMed ID: 27960048
[TBL] [Abstract][Full Text] [Related]
4. Risk Prediction for Portal Vein Thrombosis in Acute Pancreatitis Using Radial Basis Function.
Fei Y; Hu J; Gao K; Tu J; Wang W; Li WQ
Ann Vasc Surg; 2018 Feb; 47():78-84. PubMed ID: 28943487
[TBL] [Abstract][Full Text] [Related]
5. Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models.
Fei Y; Hu J; Gao K; Tu J; Li WQ; Wang W
J Crit Care; 2017 Jun; 39():115-123. PubMed ID: 28246056
[TBL] [Abstract][Full Text] [Related]
6. [Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients].
Tang CQ; Li JQ; Xu DY; Liu XB; Hou WJ; Lyu KY; Xiao SC; Xia ZF
Zhonghua Shao Shang Za Zhi; 2018 Jun; 34(6):343-348. PubMed ID: 29961290
[No Abstract] [Full Text] [Related]
7. Appropriateness of a donor liver with respect to macrosteatosis: application of artificial neural networks to US images--initial experience.
Kim SH; Lee JM; Kim JH; Kim KG; Han JK; Lee KH; Park SH; Yi NJ; Suh KS; An SK; Kim YJ; Son KR; Lee HS; Choi BI
Radiology; 2005 Mar; 234(3):793-803. PubMed ID: 15665225
[TBL] [Abstract][Full Text] [Related]
8. Predicting fatal outcome in the early phase of severe acute pancreatitis by using novel prognostic models.
Halonen KI; Leppäniemi AK; Lundin JE; Puolakkainen PA; Kemppainen EA; Haapiainen RK
Pancreatology; 2003; 3(4):309-15. PubMed ID: 12890993
[TBL] [Abstract][Full Text] [Related]
9. Predicting the incidence of portosplenomesenteric vein thrombosis in patients with acute pancreatitis using classification and regression tree algorithm.
Fei Y; Gao K; Hu J; Tu J; Li WQ; Wang W; Zong GQ
J Crit Care; 2017 Jun; 39():124-130. PubMed ID: 28254727
[TBL] [Abstract][Full Text] [Related]
10. Artificial neural networks accurately predict intra-abdominal infection in moderately severe and severe acute pancreatitis.
Qiu Q; Nian YJ; Tang L; Guo Y; Wen LZ; Wang B; Chen DF; Liu KJ
J Dig Dis; 2019 Sep; 20(9):486-494. PubMed ID: 31328389
[TBL] [Abstract][Full Text] [Related]
11. Automated machine learning for early prediction of acute kidney injury in acute pancreatitis.
Zhang R; Yin M; Jiang A; Zhang S; Xu X; Liu L
BMC Med Inform Decis Mak; 2024 Jan; 24(1):16. PubMed ID: 38212745
[TBL] [Abstract][Full Text] [Related]
12. Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation.
Harrison RF; Kennedy RL
Ann Emerg Med; 2005 Nov; 46(5):431-9. PubMed ID: 16271675
[TBL] [Abstract][Full Text] [Related]
13. Comparison of artificial neural networks with logistic regression in prediction of in-hospital death after percutaneous transluminal coronary angioplasty.
Freeman RV; Eagle KA; Bates ER; Werns SW; Kline-Rogers E; Karavite D; Moscucci M
Am Heart J; 2000 Sep; 140(3):511-20. PubMed ID: 10966555
[TBL] [Abstract][Full Text] [Related]
14. Prediction of severe acute pancreatitis at admission to hospital using artificial neural networks.
Andersson B; Andersson R; Ohlsson M; Nilsson J
Pancreatology; 2011; 11(3):328-35. PubMed ID: 21757970
[TBL] [Abstract][Full Text] [Related]
15. The role of artificial neural networks in prediction of severe acute pancreatitis associated acute respiratory distress syndrome: A retrospective study.
Zou K; Ren W; Huang S; Jiang J; Xu H; Zeng X; Zhang H; Peng Y; Lü M; Tang X
Medicine (Baltimore); 2023 Jul; 102(29):e34399. PubMed ID: 37478242
[TBL] [Abstract][Full Text] [Related]
16. Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease.
Søreide K; Thorsen K; Søreide JA
Eur J Trauma Emerg Surg; 2015 Feb; 41(1):91-8. PubMed ID: 25621078
[TBL] [Abstract][Full Text] [Related]
17. Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals.
Yin M; Zhang R; Zhou Z; Liu L; Gao J; Xu W; Yu C; Lin J; Liu X; Xu C; Zhu J
Front Cell Infect Microbiol; 2022; 12():886935. PubMed ID: 35755847
[TBL] [Abstract][Full Text] [Related]
18. Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis.
Qiu Q; Nian YJ; Guo Y; Tang L; Lu N; Wen LZ; Wang B; Chen DF; Liu KJ
BMC Gastroenterol; 2019 Jul; 19(1):118. PubMed ID: 31272385
[TBL] [Abstract][Full Text] [Related]
19. Building and verifying a severity prediction model of acute pancreatitis (AP) based on BISAP, MEWS and routine test indexes.
Ye JF; Zhao YX; Ju J; Wang W
Clin Res Hepatol Gastroenterol; 2017 Oct; 41(5):585-591. PubMed ID: 28918932
[TBL] [Abstract][Full Text] [Related]
20. Early prediction of severe acute pancreatitis using machine learning.
Thapa R; Iqbal Z; Garikipati A; Siefkas A; Hoffman J; Mao Q; Das R
Pancreatology; 2022 Jan; 22(1):43-50. PubMed ID: 34690046
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
