209 related articles for article (PubMed ID: 14640391)
1. HIV lipodystrophy case definition using artificial neural network modelling.
Ioannidis JP; Trikalinos TA; Law M; Carr A;
Antivir Ther; 2003 Oct; 8(5):435-41. PubMed ID: 14640391
[TBL] [Abstract][Full Text] [Related]
2. Easy and low-cost identification of metabolic syndrome in patients treated with second-generation antipsychotics: artificial neural network and logistic regression models.
Lin CC; Bai YM; Chen JY; Hwang TJ; Chen TT; Chiu HW; Li YC
J Clin Psychiatry; 2010 Mar; 71(3):225-34. PubMed ID: 19814949
[TBL] [Abstract][Full Text] [Related]
3. A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients.
Selker HP; Griffith JL; Patil S; Long WJ; D'Agostino RB
J Investig Med; 1995 Oct; 43(5):468-76. PubMed ID: 8528758
[TBL] [Abstract][Full Text] [Related]
4. Practical applications of the HIV lipodystrophy case definition.
Puls RL; Carr A
AIDS Read; 2003 Oct; 13(10):480-1, 486-7, 491-3. PubMed ID: 14596225
[TBL] [Abstract][Full Text] [Related]
5. Comparison of artificial neural networks with logistic regression in prediction of gallbladder disease among obese patients.
Liew PL; Lee YC; Lin YC; Lee TS; Lee WJ; Wang W; Chien CW
Dig Liver Dis; 2007 Apr; 39(4):356-62. PubMed ID: 17317348
[TBL] [Abstract][Full Text] [Related]
6. Mitochondrial DNA assessment in adipocytes and peripheral blood mononuclear cells of HIV-infected patients with lipodystrophy according to a validated case definition.
Casula M; van der Valk M; Wit FW; Nievaard MA; Reiss P
HIV Med; 2007 Jan; 8(1):32-7. PubMed ID: 17305930
[TBL] [Abstract][Full Text] [Related]
7. Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room.
Jaimes F; Farbiarz J; Alvarez D; Martínez C
Crit Care; 2005 Apr; 9(2):R150-6. PubMed ID: 15774048
[TBL] [Abstract][Full Text] [Related]
8. Fat mass ratio: an objective tool to define lipodystrophy in hiv-infected patients under antiretroviral therapy.
Freitas P; Santos AC; Carvalho D; Pereira J; Marques R; Martinez E; Sarmento A; Medina JL
J Clin Densitom; 2010; 13(2):197-203. PubMed ID: 20347364
[TBL] [Abstract][Full Text] [Related]
9. Neural networks as predictors of outcomes in alcoholic patients with severe liver disease.
Lapuerta P; Rajan S; Bonacini M
Hepatology; 1997 Feb; 25(2):302-6. PubMed ID: 9021938
[TBL] [Abstract][Full Text] [Related]
10. [Comparison of multiple prediction models for hypertension (Neural networks, logistic regression and flexible discriminant analyses)].
Türe M; Kurt I; Yavuz E; Kürüm T
Anadolu Kardiyol Derg; 2005 Mar; 5(1):24-8. PubMed ID: 15755697
[TBL] [Abstract][Full Text] [Related]
11. Risk factor identification and mortality prediction in cardiac surgery using artificial neural networks.
Nilsson J; Ohlsson M; Thulin L; Höglund P; Nashef SA; Brandt J
J Thorac Cardiovasc Surg; 2006 Jul; 132(1):12-9. PubMed ID: 16798296
[TBL] [Abstract][Full Text] [Related]
12. Evaluation of the HIV lipodystrophy case definition in a placebo-controlled, 144-week study in antiretroviral-naive adults.
Law M; Puls R; Cheng AK; Cooper DA; Carr A
Antivir Ther; 2006; 11(2):179-86. PubMed ID: 16640099
[TBL] [Abstract][Full Text] [Related]
13. Comparison of dual-energy X-ray absorptiometry machines for measuring fat distribution changes of HIV-associated lipodystrophy.
Yang Y; Zhu WD; Paton NI
Antivir Ther; 2004 Oct; 9(5):771-8. PubMed ID: 15535415
[TBL] [Abstract][Full Text] [Related]
14. Prediction of significant fibrosis in hepatitis C virus infected liver transplant recipients by artificial neural network analysis of clinical factors.
Piscaglia F; Cucchetti A; Benlloch S; Vivarelli M; Berenguer J; Bolondi L; Pinna AD; Berenguer M
Eur J Gastroenterol Hepatol; 2006 Dec; 18(12):1255-61. PubMed ID: 17099373
[TBL] [Abstract][Full Text] [Related]
15. Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room.
Green M; Björk J; Forberg J; Ekelund U; Edenbrandt L; Ohlsson M
Artif Intell Med; 2006 Nov; 38(3):305-18. PubMed ID: 16962295
[TBL] [Abstract][Full Text] [Related]
16. Clinical assessment of HIV-associated lipodystrophy syndrome: bioelectrical impedance analysis, anthropometry and clinical scores.
Schwenk A; Breuer P; Kremer G; Ward L
Clin Nutr; 2001 Jun; 20(3):243-9. PubMed ID: 11407871
[TBL] [Abstract][Full Text] [Related]
17. Advanced integrated technique in breast cancer thermography.
Ng EY; Kee EC
J Med Eng Technol; 2008; 32(2):103-14. PubMed ID: 17852648
[TBL] [Abstract][Full Text] [Related]
18. HIV lipodystrophy diagnosis and management. Body composition and metabolic alterations: diagnosis and management.
Engelson ES
AIDS Read; 2003 Apr; 13(4 Suppl):S10-4. PubMed ID: 12762288
[TBL] [Abstract][Full Text] [Related]
19. [Definition of lipodystrophy syndrome in HIV-infected adults].
Blasko M
Dtsch Med Wochenschr; 2003 May; 128(18):974. PubMed ID: 12755135
[No Abstract] [Full Text] [Related]
20. The use of artificial neural network analysis to improve the predictive accuracy of prostate biopsy in the Japanese population.
Matsui Y; Utsunomiya N; Ichioka K; Ueda N; Yoshimura K; Terai A; Arai Y
Jpn J Clin Oncol; 2004 Oct; 34(10):602-7. PubMed ID: 15591458
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
