A Fuzzy Discrete Event Systems Approach to Selecting Second-Round Combination Antiretroviral Therapy for HIV/AIDS Patients

We have recently pioneered the development of an innovative general-purpose decision-making and optimization technology, called fuzzy discrete event systems (FDES). In the previous papers, we reported results of applying FDES to selecting optimal first-round regimens for HIV/AIDS patients. In the present paper, we describe our further effort to apply the FDES framework to the second-round treatment, which is more challenging primarily due to drug resistance that occurs during the first-round treatment. We focused on five currently popular second-round regimens and 16 different treatment objectives. Two clinical AIDS experts on our team independently rated the five regimens as first-choice to fifth-choice regimen for each objective and their selections were used as golden standard. We used a genetic algorithm to optimize 20 parameters of our system named AIDS-FDES so that its regimen choices best matched those of the experts individually (i.e., through two different parameters sets). Our preliminary results showed that for the first-choice regimens, the exact agreements between AIDS-FDES and expert A and expert B were 87.5% and 100%, respectively, whereas the mean agreement rate for the five regimens was 77.5% and 80.1%, respectively. For all the five regimens, the agreement within one preference level (i.e., one physician´s second choice is another physician´s first or third choice), which was an overall agreement measure, for experts A and B was 92.5% and 96.3%, respectively. We also optimized and used just one parameter set to match AIDS-FDES to both the experts simultaneously. The agreement within one preference level for expert A was 90% and 86.3% for expert B. In order to adjust for any agreement likely to occur simply by chance, a weighted Cohen´s Kappa was used. The results for the expert´s combined selections relative to AIDS-FDES demonstrated that the specialists agreed with the treatment selection made by the computer system with a weighted Cohen´s Ka- ppa of 0.78 (95% confidence interval is [0.69, 0.87]), which indicates that the expert´s combined agreement with the system´s choices (beyond that expected by chance) was importantly improved over that of either expert´s agreement with each other