HIV Therapy Selection with Incomplete Data

HIV is the cause of a global pandemic that currently affects more than 36 million people worldwide. Effective treatment is key to combatting the virus and preventing further infection. Combination therapy is used to overcome drug-resistance. However, therapy selection is difficult because: 1. There are a large number of drug combinations. 2. Experience using drugs may be limited. The problem is exacerbated when patient treatment histories are incomplete. The aim of this project is to demonstrate that machine learning techniques can be used to support therapy selection and overcome the problems posed by missing data.