265 related articles for article (PubMed ID: 35653504)
1. EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis.
Kui B; Pintér J; Molontay R; Nagy M; Farkas N; Gede N; Vincze Á; Bajor J; Gódi S; Czimmer J; Szabó I; Illés A; Sarlós P; Hágendorn R; Pár G; Papp M; Vitális Z; Kovács G; Fehér E; Földi I; Izbéki F; Gajdán L; Fejes R; Németh BC; Török I; Farkas H; Mickevicius A; Sallinen V; Galeev S; Ramírez-Maldonado E; Párniczky A; Erőss B; Hegyi PJ; Márta K; Váncsa S; Sutton R; Szatmary P; Latawiec D; Halloran C; de-Madaria E; Pando E; Alberti P; Gómez-Jurado MJ; Tantau A; Szentesi A; Hegyi P;
Clin Transl Med; 2022 Jun; 12(6):e842. PubMed ID: 35653504
[TBL] [Abstract][Full Text] [Related]
2. [Application of machine learning model based on XGBoost algorithm in early prediction of patients with acute severe pancreatitis].
Gao X; Lin J; Wu A; Gu H; Liu X; Yin M; Zhou Z; Zhang R; Xu C; Zhu J
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Apr; 35(4):421-426. PubMed ID: 37308200
[TBL] [Abstract][Full Text] [Related]
3. Early prediction of acute necrotizing pancreatitis by artificial intelligence: a prospective cohort-analysis of 2387 cases.
Kiss S; Pintér J; Molontay R; Nagy M; Farkas N; Sipos Z; Fehérvári P; Pecze L; Földi M; Vincze Á; Takács T; Czakó L; Izbéki F; Halász A; Boros E; Hamvas J; Varga M; Mickevicius A; Faluhelyi N; Farkas O; Váncsa S; Nagy R; Bunduc S; Hegyi PJ; Márta K; Borka K; Doros A; Hosszúfalusi N; Zubek L; Erőss B; Molnár Z; Párniczky A; Hegyi P; Szentesi A;
Sci Rep; 2022 May; 12(1):7827. PubMed ID: 35552440
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Comparison of Four Machine Learning Techniques for Prediction of Intensive Care Unit Length of Stay in Heart Transplantation Patients.
Wang K; Yan LZ; Li WZ; Jiang C; Wang NN; Zheng Q; Dong NG; Shi JW
Front Cardiovasc Med; 2022; 9():863642. PubMed ID: 35800164
[TBL] [Abstract][Full Text] [Related]
6. CVD22: Explainable artificial intelligence determination of the relationship of troponin to D-Dimer, mortality, and CK-MB in COVID-19 patients.
Kırboğa KK; Küçüksille EU; Naldan ME; Işık M; Gülcü O; Aksakal E
Comput Methods Programs Biomed; 2023 May; 233():107492. PubMed ID: 36965300
[TBL] [Abstract][Full Text] [Related]
7. Prediction of the severity of acute pancreatitis using machine learning models.
Zhou Y; Han F; Shi XL; Zhang JX; Li GY; Yuan CC; Lu GT; Hu LH; Pan JJ; Xiao WM; Yao GH
Postgrad Med; 2022 Sep; 134(7):703-710. PubMed ID: 35801388
[TBL] [Abstract][Full Text] [Related]
8. Early prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence.
İnce AT; Silahtaroğlu G; Seven G; Koçhan K; Yıldız K; Şentürk H
Pancreatology; 2023 Mar; 23(2):176-186. PubMed ID: 36610872
[TBL] [Abstract][Full Text] [Related]
9. Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein.
Pearce CB; Gunn SR; Ahmed A; Johnson CD
Pancreatology; 2006; 6(1-2):123-31. PubMed ID: 16327290
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule.
Chang CH; Chen CJ; Ma YS; Shen YT; Sung MI; Hsu CC; Lin HJ; Chen ZC; Huang CC; Liu CF
Acad Emerg Med; 2024 Feb; 31(2):149-155. PubMed ID: 37885118
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP.
Zhang G; Shi Y; Yin P; Liu F; Fang Y; Li X; Zhang Q; Zhang Z
Front Oncol; 2022; 12():944569. PubMed ID: 35957890
[TBL] [Abstract][Full Text] [Related]
14. Application of interpretable machine learning for early prediction of prognosis in acute kidney injury.
Hu C; Tan Q; Zhang Q; Li Y; Wang F; Zou X; Peng Z
Comput Struct Biotechnol J; 2022; 20():2861-2870. PubMed ID: 35765651
[TBL] [Abstract][Full Text] [Related]
15. Explainable machine learning approach to predict extubation in critically ill ventilated patients: a retrospective study in central Taiwan.
Pai KC; Su SA; Chan MC; Wu CL; Chao WC
BMC Anesthesiol; 2022 Nov; 22(1):351. PubMed ID: 36376785
[TBL] [Abstract][Full Text] [Related]
16. Acute Pancreatitis Severity Prediction: It Is Time to Use Artificial Intelligence.
Tarján D; Hegyi P
J Clin Med; 2022 Dec; 12(1):. PubMed ID: 36615090
[TBL] [Abstract][Full Text] [Related]
17. Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.
Shi H; Yang D; Tang K; Hu C; Li L; Zhang L; Gong T; Cui Y
Clin Nutr; 2022 Jan; 41(1):202-210. PubMed ID: 34906845
[TBL] [Abstract][Full Text] [Related]
18. [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]
19. Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research.
Yagin B; Yagin FH; Colak C; Inceoglu F; Kadry S; Kim J
Diagnostics (Basel); 2023 Oct; 13(21):. PubMed ID: 37958210
[TBL] [Abstract][Full Text] [Related]
20. Prediction of the development of acute kidney injury following cardiac surgery by machine learning.
Tseng PY; Chen YT; Wang CH; Chiu KM; Peng YS; Hsu SP; Chen KL; Yang CY; Lee OK
Crit Care; 2020 Jul; 24(1):478. PubMed ID: 32736589
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
