Optimizing HIV Treatment Monitoring under Resource Constraints

Effective use of antiretroviral therapy is critical for managing and preventing the spread of HIV in the developing world. Research in this domain includes developing methods that make optimal use of diagnostic tests having limited availability to monitor the effectiveness of therapy and to prompt a change in regimen when it is warranted. Machine learning methods can be used to derive the best strategies to treat HIV along the HIV treatment cascade.  Successful implementation is expected to improve patient outcomes and help to prevent the spread of treatment-resistant strains of HIV.

Joe Hogan Tao Liu Stavroula Chrysanthopoulou Jon Steingrimsson

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