111 related articles for article (PubMed ID: 21197361)
1. Development of a Bayesian model to estimate health care outcomes in the severely wounded.
Stojadinovic A; Eberhardt J; Brown TS; Hawksworth JS; Gage F; Tadaki DK; Forsberg JA; Davis TA; Potter BK; Dunne JR; Elster EA
J Multidiscip Healthc; 2010 Aug; 3():125-35. PubMed ID: 21197361
[TBL] [Abstract][Full Text] [Related]
2. Estimating survival in patients with operable skeletal metastases: an application of a bayesian belief network.
Forsberg JA; Eberhardt J; Boland PJ; Wedin R; Healey JH
PLoS One; 2011; 6(5):e19956. PubMed ID: 21603644
[TBL] [Abstract][Full Text] [Related]
3. Towards precision medicine: Accurate predictive modeling of infectious complications in combat casualties.
Dente CJ; Bradley M; Schobel S; Gaucher B; Buchman T; Kirk AD; Elster E
J Trauma Acute Care Surg; 2017 Oct; 83(4):609-616. PubMed ID: 28538622
[TBL] [Abstract][Full Text] [Related]
4. Preventing Heterotopic Ossification in Combat Casualties-Which Models Are Best Suited for Clinical Use?
Alfieri KA; Potter BK; Davis TA; Wagner MB; Elster EA; Forsberg JA
Clin Orthop Relat Res; 2015 Sep; 473(9):2807-13. PubMed ID: 25917420
[TBL] [Abstract][Full Text] [Related]
5. Using machine-learned bayesian belief networks to predict perioperative risk of clostridium difficile infection following colon surgery.
Steele S; Bilchik A; Eberhardt J; Kalina P; Nissan A; Johnson E; Avital I; Stojadinovic A
Interact J Med Res; 2012 Sep; 1(2):e6. PubMed ID: 23611947
[TBL] [Abstract][Full Text] [Related]
6. Development of a Bayesian Belief Network Model for personalized prognostic risk assessment in colon carcinomatosis.
Stojadinovic A; Nissan A; Eberhardt J; Chua TC; Pelz JO; Esquivel J
Am Surg; 2011 Feb; 77(2):221-30. PubMed ID: 21337884
[TBL] [Abstract][Full Text] [Related]
7. Clinical decision support and individualized prediction of survival in colon cancer: bayesian belief network model.
Stojadinovic A; Bilchik A; Smith D; Eberhardt JS; Ward EB; Nissan A; Johnson EK; Protic M; Peoples GE; Avital I; Steele SR
Ann Surg Oncol; 2013 Jan; 20(1):161-74. PubMed ID: 22899001
[TBL] [Abstract][Full Text] [Related]
8. Prognostication of traumatic brain injury outcomes in older trauma patients: A novel risk assessment tool based on initial cranial CT findings.
Stawicki SP; Wojda TR; Nuschke JD; Mubang RN; Cipolla J; Hoff WS; Hoey BA; Thomas PG; Sweeney J; Ackerman D; Hosey J; Falowski S
Int J Crit Illn Inj Sci; 2017; 7(1):23-31. PubMed ID: 28382256
[TBL] [Abstract][Full Text] [Related]
9. Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan.
Kuo PJ; Wu SC; Chien PC; Rau CS; Chen YC; Hsieh HY; Hsieh CH
BMJ Open; 2018 Jan; 8(1):e018252. PubMed ID: 29306885
[TBL] [Abstract][Full Text] [Related]
10. [Establishment of an early risk prediction model for bloodstream infection and analysis of its predictive value in patients with extremely severe burns].
Zhang Y; Ma ZZ; Wu BW; Dou Y; Zhang Q; Yang LY; Chen EZ
Zhonghua Shao Shang Za Zhi; 2021 Jun; 37(6):530-537. PubMed ID: 34139830
[No Abstract] [Full Text] [Related]
11. A machine learning approach to predict early outcomes after pituitary adenoma surgery.
Hollon TC; Parikh A; Pandian B; Tarpeh J; Orringer DA; Barkan AL; McKean EL; Sullivan SE
Neurosurg Focus; 2018 Nov; 45(5):E8. PubMed ID: 30453460
[TBL] [Abstract][Full Text] [Related]
12. Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries.
Feng JZ; Wang Y; Peng J; Sun MW; Zeng J; Jiang H
J Crit Care; 2019 Dec; 54():110-116. PubMed ID: 31408805
[TBL] [Abstract][Full Text] [Related]
13. Development of a Bayesian classifier for breast cancer risk stratification: a feasibility study.
Stojadinovic A; Eberhardt C; Henry L; Eberhardt J; Elster EA; Peoples GE; Nissan A; Shriver CD
Eplasty; 2010 Mar; 10():e25. PubMed ID: 20418939
[TBL] [Abstract][Full Text] [Related]
14. Emergency department triage prediction of clinical outcomes using machine learning models.
Raita Y; Goto T; Faridi MK; Brown DFM; Camargo CA; Hasegawa K
Crit Care; 2019 Feb; 23(1):64. PubMed ID: 30795786
[TBL] [Abstract][Full Text] [Related]
15. Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method.
Gupta A; Slater JJ; Boyne D; Mitsakakis N; Béliveau A; Druzdzel MJ; Brenner DR; Hussain S; Arora P
Med Decis Making; 2019 Nov; 39(8):1032-1044. PubMed ID: 31619130
[No Abstract] [Full Text] [Related]
16. Bayesian modeling of pretransplant variables accurately predicts kidney graft survival.
Brown TS; Elster EA; Stevens K; Graybill JC; Gillern S; Phinney S; Salifu MO; Jindal RM
Am J Nephrol; 2012; 36(6):561-9. PubMed ID: 23221105
[TBL] [Abstract][Full Text] [Related]
17. Estimating risk of severe neonatal morbidity in preterm births under 32 weeks of gestation.
Hamilton EF; Dyachenko A; Ciampi A; Maurel K; Warrick PA; Garite TJ
J Matern Fetal Neonatal Med; 2020 Jan; 33(1):73-80. PubMed ID: 29886760
[No Abstract] [Full Text] [Related]
18. [A new score system for prediction of death in patients with severe trauma: the value of death warning score].
Xueyuan L; Yu S; Min G; Ya X; He J; Miao C; Huaping L; Tao C
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2015 Nov; 27(11):890-4. PubMed ID: 27132455
[TBL] [Abstract][Full Text] [Related]
19. Modern modeling techniques had limited external validity in predicting mortality from traumatic brain injury.
van der Ploeg T; Nieboer D; Steyerberg EW
J Clin Epidemiol; 2016 Oct; 78():83-89. PubMed ID: 26987507
[TBL] [Abstract][Full Text] [Related]
20. Performance comparison between Logistic regression, decision trees, and multilayer perceptron in predicting peripheral neuropathy in type 2 diabetes mellitus.
Li CP; Zhi XY; Ma J; Cui Z; Zhu ZL; Zhang C; Hu LP
Chin Med J (Engl); 2012 Mar; 125(5):851-7. PubMed ID: 22490586
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
