Abstract
The goal of this paper was to study how machine learning techniques have been applied in PrEP (Pre-exposure prophylaxis) and HIV prediction to identify individuals who are at a higher risk of acquiring HIV infection and to optimize the used of PrEP, which is an effective method of preventing HIV transmission. The results indicate that machine learning has been used to HIV risk, optimizing PrEP use, developing personalized PrEP regimens, and identifying PrEP candidates. In predicting HIV risk, machine learning algorithms have been developed to predict HIV risk based on various factors such as demographic information, sexual behavior and drug use. The models can identify individuals who are at a higher risk of acquiring HIV infection and can be used to target interventions such as PrEP to those who are most in need. Regarding optimizing PrEP use, machine learning has been utilized to optimize PrEP usage by identifying the factors that are associated with adherence to PrEP. These models can help healthcare providers to tailor their interventions to promote PrEP adherence and improve its effectiveness. In addition, machine learning techniques have been used to develop personalized PrEP regimens based on an individual’s HIV risk profile. These models can help healthcare providers to optimize PrEP use and reduce the risk of HIV transmission. It was also established that machine learning models have been used to identify individuals who are most likely to benefit from PrEP. These models can help healthcare providers to target PrEP interventions to those who are likely to benefit from them.
Published Version
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