title: Neural networks in the assessment of HIV immunopathology.
abstract: Surrogate markers are by definition quantifiable laboratory variables that have clinical and biological relevance to disease outcomes. Virologic and immunologic surrogate markers have proven useful in following HIV-associated viral burden, immune dysregulation, dysfunction and deficiency. Monitoring of sequential changes in these markers and their interrelationships may provide significant information about viral-host-drug dynamics. The complexity and fluidity of these changes necessitates that an efficient means be developed for their monitoring. We therefore generated a neural network-based model for assessing host dynamics over time and compared its performance with that of a multiple regression model. Both modeling approaches were applied to the actual, non-filtered, clinical observations on 58 HIV-infected individuals treated consistently with Highly Active Anti-Retroviral Therapy (HAART), for a period of over-52 weeks resulting in an average of 16 observations per patient throughout this time span. Results demonstrated that the neural network was at least as accurate as a multi-regression model. Since our dataset was modest in size we also believe that neural networks warrant further consideration for modeling the complexity of HIV-host dynamics on larger datasets.
authors.affiliation: Immune Deficiency Treatment Center, McGill University Health Center, Montreal, Quebec, Canada.
authors.forename: G
authors.initials: G
authors.lastname: Hatzakis
authors.forename: C
authors.initials: C
authors.lastname: Tsoukas
mesh_terms: D023241:Antiretroviral Therapy, Highly Active; D015415:Biomarkers; D015658:HIV Infections / Q000188:drug therapy / Q000276:immunology* / Q000821:virology*; D006801:Humans; D016014:Linear Models; D016571:Neural Networks, Computer
chemical_list: D015415:Biomarkers