135 related articles for article (PubMed ID: 23793529)
41. Resuscitation with a hemoglobin-based oxygen carrier after traumatic brain injury.
King DR; Cohn SM; Proctor KG
J Trauma; 2005 Sep; 59(3):553-60; discussion 560-2. PubMed ID: 16361895
[TBL] [Abstract][Full Text] [Related]
42. A decision support system to facilitate management of patients with acute gastrointestinal bleeding.
Chu A; Ahn H; Halwan B; Kalmin B; Artifon EL; Barkun A; Lagoudakis MG; Kumar A
Artif Intell Med; 2008 Mar; 42(3):247-59. PubMed ID: 18063351
[TBL] [Abstract][Full Text] [Related]
43. Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure.
Qiu H; Luo L; Su Z; Zhou L; Wang L; Chen Y
BMC Med Inform Decis Mak; 2020 May; 20(1):83. PubMed ID: 32357880
[TBL] [Abstract][Full Text] [Related]
44. Prediction of persistent hemodynamic depression after carotid angioplasty and stenting using artificial neural network model.
Jeon JP; Kim C; Oh BD; Kim SJ; Kim YS
Clin Neurol Neurosurg; 2018 Jan; 164():127-131. PubMed ID: 29223792
[TBL] [Abstract][Full Text] [Related]
45. Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review.
Buchlak QD; Esmaili N; Leveque JC; Farrokhi F; Bennett C; Piccardi M; Sethi RK
Neurosurg Rev; 2020 Oct; 43(5):1235-1253. PubMed ID: 31422572
[TBL] [Abstract][Full Text] [Related]
46. Eight hours of hypotensive versus normotensive resuscitation in a porcine model of controlled hemorrhagic shock.
Skarda DE; Mulier KE; George ME; Bellman GJ
Acad Emerg Med; 2008 Sep; 15(9):845-52. PubMed ID: 19244635
[TBL] [Abstract][Full Text] [Related]
47. Prognostic value of blood lactate, base deficit, and oxygen-derived variables in an LD50 model of penetrating trauma.
Moomey CB; Melton SM; Croce MA; Fabian TC; Proctor KG
Crit Care Med; 1999 Jan; 27(1):154-61. PubMed ID: 9934910
[TBL] [Abstract][Full Text] [Related]
48. A survival prediction model of hemorrhagic shock in rats using a logistic regression equation.
Lee TH; Lee JH; Chung SW; Noh HW; Shim YW; Kim DW
Annu Int Conf IEEE Eng Med Biol Soc; 2009; 2009():1274-7. PubMed ID: 19964753
[TBL] [Abstract][Full Text] [Related]
49. [Machine Learning Methods for Prediction of Hospital Mortality in Patients with Coronary Heart Disease after Coronary Artery Bypass Grafting].
Geltser BI; Shahgeldyan KJ; Rublev VY; Kotelnikov VN; Krieger AB; Shirobokov VG
Kardiologiia; 2020 Nov; 60(10):38-46. PubMed ID: 33228504
[TBL] [Abstract][Full Text] [Related]
50. Beneficial effects of combined thromboxane and leukotriene receptor antagonism in hemorrhagic shock.
Patel JP; Beck LD; Briglia FA; Hock CE
Crit Care Med; 1995 Feb; 23(2):231-7. PubMed ID: 7867347
[TBL] [Abstract][Full Text] [Related]
51. Evaluation of the use of shock index in identifying acute blood loss in healthy blood donor dogs.
McGowan EE; Marryott K; Drobatz KJ; Reineke EL
J Vet Emerg Crit Care (San Antonio); 2017 Sep; 27(5):524-531. PubMed ID: 28795781
[TBL] [Abstract][Full Text] [Related]
52. Prediction models for high risk of suicide in Korean adolescents using machine learning techniques.
Jung JS; Park SJ; Kim EY; Na KS; Kim YJ; Kim KG
PLoS One; 2019; 14(6):e0217639. PubMed ID: 31170212
[TBL] [Abstract][Full Text] [Related]
53. Controlled resuscitation for uncontrolled hemorrhagic shock.
Burris D; Rhee P; Kaufmann C; Pikoulis E; Austin B; Eror A; DeBraux S; Guzzi L; Leppäniemi A
J Trauma; 1999 Feb; 46(2):216-23. PubMed ID: 10029024
[TBL] [Abstract][Full Text] [Related]
54. Supervised classification techniques for prediction of mortality in adult patients with sepsis.
Rodríguez A; Mendoza D; Ascuntar J; Jaimes F
Am J Emerg Med; 2021 Jul; 45():392-397. PubMed ID: 33036848
[TBL] [Abstract][Full Text] [Related]
55. Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model.
Bozorgmehr A; Thielmann A; Weltermann B
PLoS One; 2021; 16(5):e0250842. PubMed ID: 33945572
[TBL] [Abstract][Full Text] [Related]
56. A novel mathematical model to predict prognosis of burnt patients based on logistic regression and support vector machine.
Huang Y; Zhang L; Lian G; Zhan R; Xu R; Huang Y; Mitra B; Wu J; Luo G
Burns; 2016 Mar; 42(2):291-9. PubMed ID: 26774603
[TBL] [Abstract][Full Text] [Related]
57. Prediction of intracerebral hemorrhage following thrombolytic therapy for acute ischemic stroke using multiple artificial neural networks.
Dharmasaroja P; Dharmasaroja PA
Neurol Res; 2012 Mar; 34(2):120-8. PubMed ID: 22333462
[TBL] [Abstract][Full Text] [Related]
58. Predicting coronary artery disease: a comparison between two data mining algorithms.
Ayatollahi H; Gholamhosseini L; Salehi M
BMC Public Health; 2019 Apr; 19(1):448. PubMed ID: 31035958
[TBL] [Abstract][Full Text] [Related]
59. Effects of starch nanospheres solution on hemodynamic values in rats with hemorrhagic shock: a preliminary study in hemorrhagic shock resuscitation.
Li Y; Li XM; Xu LX
J Surg Res; 2013 May; 181(1):142-5. PubMed ID: 22748599
[TBL] [Abstract][Full Text] [Related]
60. Increased circulating D-lactate levels predict risk of mortality after hemorrhage and surgical trauma in baboons.
Sobhian B; Kröpfl A; Hölzenbein T; Khadem A; Redl H; Bahrami S
Shock; 2012 May; 37(5):473-7. PubMed ID: 22266971
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
