A fuzzy discrete event system for HIV/AIDS treatment

The United Nations estimates that 38 million people worldwide are infected with HIV/AIDS, and that more than 22 million have died. Like most diseases, treatment decision-making for this disease is currently more an art than science. This is partially because every patient is unique, with his/her own history, set of genetic traits, predisposition to side effects, and prognosis. We reported previously how we had developed a theoretical framework of novel fuzzy discrete event systems, which are knowledge-based [1, 2]. We showed how to apply it to develop a part of HIV/AIDS regimen selection system for treating antiretroviral-naïve patients [3, 4]. In the present paper, we describe our recent development - we have furthered the system design by adding a Genetic-Algorithm-Based Regimen Selection Optimizer and a Treatment Objectives Classifier to the system. The full system is capable of prescribing a regimen for any given patient. The Optimizer enables the system to either emulate an individual doctor's decision-making or generate a regimen that simultaneously satisfies diverse treatment preferences of multiple physicians to the maximum extent. We show the promising preliminary results of retrospective evaluation of the system using 48 treatment-naïve patients who started antiretroviral treatment in our AIDS Clinic in 2001. Our fuzzy DES approach possesses a number of unique features and advantages that are especially important to medical applications: (1) higher flexibility and scalability, and (2) easier knowledge upgrade for accommodating fast treatment strategy evolution with minimal system modification. These are particularly important to HIV/AIDS treatment as the U.S. Public Health Service updates its treatment guidelines at least once a year.