How machine learning can be leveraged for HIV care

Machine learning in healthcare is a huge boon for doctors and pharmaceutical companies in humanity’s long battle against HIV and AIDS.

As COVID-19 fades into the lives of people around the world, it’s easy to momentarily forget another deadly epidemic that has plagued since the early 1970s. Over the next five decades after emerging as a threat major health problem, HIV/AIDS has claimed the lives of nearly 40 million people worldwide. Encouragingly, infection and death rates in recent times have more than halved since peaking in 1997. The main reasons for this decline are greater preventive awareness of HIV transmission routes, the formulation of super- modern life-prolonging medicines and the high levels of care given by health experts during blood transfusions, organ transplants or other procedures involving the exchange of bodily fluids between people. Today, machine learning promises to further improve the scenario for AIDS patients. Over the past decade, the involvement of machine learning in healthcare has steadily grown. Today, AI and machine learning enable doctors to treat patients with diabetes, paralysis, cancer, and other similar health conditions. AIDS, as you may know, is a completely different beast due to its inconsistent and changeable nature once it infects a host. However, machine learning algorithms can still be useful for HIV diagnosis and care to prolong patients’ lives as long as possible.

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Analysis of health records to find patterns of HIV signaling

A patient’s long-term medical records may contain evidence of possible HIV infection. For example, some symptoms, such as unexplained weight loss, low white blood cell count, frequent sweating and arthritis, are seen at a very early stage in HIV-positive patients. There is always the possibility that such symptoms will be overlooked and seen as unrelated issues. The implication of machine learning in healthcare allows doctors to sift through all of a patient’s digitized health records to find patterns of HIV signaling. So, even if a payout has mild symptoms such as profuse night sweats or frequent diarrhea, doctors can perform blood tests just to make sure whether an individual is infected or not. In this way, machine learning can lead to HIV-related diagnosis at an early stage.

Predictive analysis to concoct an antiretroviral cocktail

Anti-HIV cocktails are a mixture of several antiretroviral drugs in specific proportions. After concoction, the cocktails are administered to HIV-positive patients to counter the mutations of the virus in their bloodstream. In this app, machine learning is used to predict the percentage of various drugs that should be used to create a concoction that will counter the effects of a specific HIV mutation in a given patient’s body. Predictive analysis is performed based on several factors – stability of the patient’s immune system, allergies, prospective reaction to a cocktail based on medical history or health status, and HIV mutation.

Eventually, health care can find a definitive cure for HIV AIDS. Technology and, more importantly, data will play a key role in this discovery. As you can see, applications involving machine learning in healthcare are already showing today what is possible in HIV and AIDS care in the future.

Sherry J. Basler