The continued growth of machine learning in healthcare is a huge positive for doctors and pharma companies in humanity’s long-raging battle against HIV and AIDS.
While COVID-19 fades in and out of people’s lives globally, it is easy to momentarily forget about another deadly epidemic that has prevailed since the early 1970s. Over the next five decades after emerging as a major health threat, HIV AIDS has claimed close to 40 million lives worldwide. Encouragingly, the infection and fatality rates in recent times have reduced by more than half after peaking in 1997.
The major reasons for this decline are greater preventive awareness of HIV transmission routes, the formulation of modern life-prolonging super-drugs and the high levels of care taken by healthcare experts during blood transfusion, organ transplants or other procedures involving the exchange of bodily fluids between persons. Today, machine learning promises to further improve the scenario for AIDS patients. In the last decade or so, the involvement of machine learning in healthcare has progressively grown. Today, AI and machine learning enable doctors to treat patients with diabetes, paralysis, cancer and similar other health conditions. AIDS, as you may know, is a different beast altogether due to its incoherent, shape-shifting nature once it infects a host. However, machine learning algorithms can still be useful for HIV diagnosis and care for prolonging patients’ lives for as long as possible.
Health Record Analysis to Find HIV-Signaling Patterns
A patient’s long-term medical records may contain elements that are indicative of possible HIV infection. For example, certain symptoms—such as unexplained weight loss, lower white blood count, frequent perspiration and arthritis—are seen at a very early stage in HIV-positive patients. There is always the possibility of such symptoms being overlooked and seen as unconnected issues. The involvement of machine learning in healthcare allows doctors to go through all past digitized health records of a patient to find HIV-signaling patterns. So, even if a payment shows innocuous symptoms such as heavy night sweating or frequent diarrhea, doctors can conduct blood tests just to be sure whether an individual is infected or not. In this way, machine learning can lead to HIV-related diagnosis at an early stage.
Predictive Analytics to Concoct Antiretroviral Cocktail
HIV cocktails are a mixture of multiple antiretroviral drugs in specific proportions. After concoction, the cocktails are administered to HIV-positive patients to counter the mutations of the virus within their bloodstream. In this application, machine learning is used to predict the percentage of various drugs that need to be used to create a concoction that will counter the effects of a specific HIV mutation within a given patient’s body. The predictive analysis is carried out on the basis of several factors—a patient’s immune system stability, allergies, prospective reaction to a cocktail based on past medical records or health condition,s and HIV mutation.
Eventually, healthcare may find a definitive cure for HIV AIDS. Technology and, more importantly, data will play a key role in that 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.
