Support vector machines to forecast changes in CD4 count of HIV-1 positive patients

Scientific Research and Essays

Volumn 5, Issue 17, 2010, Pages 2384-2390

  Singh, Yashik ^a   Mars, Maurice ^b  

^a UNIVERSITY OF KWAZULU NATAL   (South Africa)

^b UNIVERSITY OF KWAZULU NATAL   (South Africa)

Author keywords

CD4 lymphocyte count; e health; Forecasting; Hiv

Indexed keywords

HUMAN IMMUNODEFICIENCY VIRUS; HUMAN IMMUNODEFICIENCY VIRUS 1;

EID: 78049525526     PISSN: None     EISSN: 19922248     Source Type: Journal    
DOI: None     Document Type: Article

References (24)

1. Altman A, Däumer M, Beerenwinkel N, Peres Y, Schülter E, Büch J, Rhee SJ, Sönnerborg A, Fessel WJ, Shafer RW, Zazzi M, Kaiser R, Lengauer T (2009). Predicting the Response to Combination Antiretroviral Therapy: Retrospective Validation of geno2pheno-THEO on a Large Clinical Database, JID. 199: 999-1006.
2. Altmann A, Rosen-Zvi M, Prosperi M, Aharoni E, Neuvirth N, Schülter E, Büch J, Struck D, Peres Y, Incardona F, Sönnerborg A, Kaiser R, Zazzi M, Lengauer T (2008). Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy. PLoS ONE 3(10): e3470.
3. Bugers CJC (1998). A tutorial on support vector machines for pattern recognition, Data mining and Knowledge discovery 2:121-167.
4. Cahn P, Perez H, Casiro A, Crinberg N, Muchinik G (1991). Progression of HIV-disease: the Buenos Aires cohort study. International conference on AIDS. 8: 157 (Abstract no. PuC 8029).
5. Campbell C, Nair Y, Maimane S, and Sibiya Z (2008). Supporting people with aids and their careers in rural South Africa: Possibilities and challenges, Health and Place 14: 507-518
6. Cherkassky V, Ma Y (2004). Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw. 17(1): 113-126.
7. Daka D, Loha E (2008). Relationship between Total Lymphocyte count (TLC) and CD4 count among peoples living with HIV, Southern Ethiopia: a retrospective evaluation. AIDS Res Ther. 5: 26.
8. Fahey JL, Taylor JM, Detels R, Hofmann B, Melmed R, Nishanian P, Giorgi JV (1990). The prognostic value of cellular and serologic markers in infection with human immunodeficiency virus type 1. N. Engl. J. Med. 322: 166-172.
9. Giarelli E, Jacabs AL (2002). HIV/AIDS care in Kwazulu-Natal, South Africa: An interview with Dr. Leana Uys, JANAC, 11(4): 52-67.
10. Larder B, Wang D, Revell A, Montaner J, Harrigan R, De Wolf F, Lange J, Wegner S, Ruiz L, Pérez-Elías MJ, Emery S, Gatell J, Monforte AD, Torti C, Zazzi M, Lane C (2007). The development of artificial neural networks to predict virological response to combination HIV therapy. Antivir. Ther. 12(1): 15-24.
11. Montaner JSG, Le TN, Le N, Craib KJP, Schechter MT (1992). Application of the World Health Organisation system for HIV infection in a cohort of homosexual men in developing a prognostically meaningful staging system. AIDS, 6: 719-724.
12. Morfeldt-maringnson L, Boumlttiger B, Nilsson B, von Stedingk L (1991). Clinical signs and laboratory markers in predicting progression to AIDS in HIV-1 infected patients, Scand J. Infect. Dis., 23(4): 443-449.
13. Morgan D, Mahe C, Mayanja C, Whitworth JAG, Kilmarx PH (2002). Progression to symptomatic disease in people infected with HIV-1 in rural Uganda: prospective cohort study. BMJ; 324: 193-197.
14. Moss AR, Bacchetti P, Osmond D, Krampf W, Chaisson RE, Stites D, Wilber J, Allain JP, Carlson J (1988). Seropositivity for HIV and the development of AIDS and AIDS related condition: three year follow up of the San Francisco general hospital cohort. Br. Med. J., 296: 745-750.
15. Mwamburi DM, Ghosh M, Fauntleroy J, Gorbach SL, Wanke CA (2005). Predicting CD4 count using total lymphocyte count: A sustainable tool for clinical decisions during HAART use. Am. J. Trop. Med. Hyg., 73(1): 58-62.
16. Navia-Vazquez Z (2007). Support vector perception, Neurcomputing, 70: 1089-1095.
17. Osmond D, Charlebois E, Lang W, Shiboski S, Moss A (1993). Factors affecting changes in time from CD4 = 200 to death in two San Francisco cohorts, 1983-1992, Int. Conf. AIDS. 9: 656 (abstract no. PO-C04-2632).
18. Post FA, Wood R, Maartens G (1996). CD4 and total lymphocyte counts as predictors of HIV disease progression. Q. J. Med., 89: 505-508.
19. Rabinowitz M, Myers L, Banjevic M, Chan A (2006). Sweetkind-Singer J. Haberer J. McCann K. and Wolkowicz R., Accurate prediction of hiv-1 drug response from the reverse transcrip-tase and protease amino acid sequences using sparse models created by convex optimization, Bioinfo, 22(5): 541-549.
20. Rhee SY, Taylor J, Wadhera G, Ben-Hur A, Brutlag DL, Shafer RW (2006). Genotypic predictors of human immunodeficiency virus type 1 drug resistance. Proc. Acad. Sci. USA.
21. Rispel CL, Metcalf AC (2009). Breaking the silence: South African HIV policies and the needs of men who have sex with men, Reprod. Health, 33: 133-142.
22. 4 Counts in HIV-infected Brazilian Individuals: A Model Based on the World Health Organization Staging System. J. Acquir. Immune Defic. Syndr., 7(2): 163-168.
23. Simon-Meyer J, Odallo D (2002). Greater involvement of people living with HIV/AIDS in South Africa, Eval. Prog. Plan, 25: 3850-3855.
24. Wang D, DeGruttola V, Hammer S, Harrigan R, Larder B, Wegner S, Winslow S, Zazzi M (2002). A Collaborative HIV Resistance Response Database Initiative: Predicting Virological Response Using Neural Network Models. Poster presentation at: The XI International HIV Drug Resistance Workshop, Seville.