Abstract
The objective of this paper is to identify the parameters of a human immunodeficiency virus (HIV) evolution model from a clinical data set of patients treated with two different highly active antiretroviral therapy (HAART) protocols. After introducing a model with six state variables, a preliminary step considers the reduction of the number of parameters to be identified by means of sensitivity analysis, and then identifiability items are discussed. A nonlinear optimization-based procedure for identification is developed, which divides the unknown parameters into two families: the group dependent and the patient dependent parameters. Numerical results show that the identified model can be individually adapted to each patient and this result is promising for predicting the effects (e.g., failures or successes) of therapeutic actions.
Original language | English |
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Pages (from-to) | 432-440 |
Number of pages | 9 |
Journal | Computer Methods and Programs in Biomedicine |
Volume | 112 |
Issue number | 3 |
DOIs | |
Publication status | Published - Dec 2013 |
Keywords
- Human immunodeficiency virus control
- Modeling and identification
- Therapy optimization
ASJC Scopus subject areas
- Computer Science Applications
- Software
- Health Informatics