A fuzzy discrete event system approach to determining optimal HIV/AIDS treatment regimens.
Treatment decision-making is complex and involves many factors. A systematic decision-making and optimization technology capable of handling variations and uncertainties of patient characteristics and physician's subjectivity is currently unavailable. We recently developed a novel general-purpose fuzzy discrete event systems theory for optimal decision-making. We now apply it to develop an innovative system for medical treatment, specifically for the first round of highly active antiretroviral therapy of human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) patients involving three historically widely used regimens. The objective is to develop such a system whose regimen choice for any given patient will exactly match expert AIDS physician's selection to produce the (anticipated) optimal treatment outcome. Our regimen selection system consists of a treatment objectives classifier, fuzzy finite state machine models for treatment regimens, and a genetic-algorithm-based optimizer. 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 used the optimizer to automatically learn the values of 26 parameters of the models. The learning was based on the consensus of AIDS specialists A and B on this project, whose exact agreement was only 35%. The performance of the resulting models was first assessed. We then carried out a retrospective study of the entire system using all the qualifying patients treated in our institution's AIDS Clinical Center in 2001. A total of 35 patients were treated by 13 specialists using the regimens (four and eight patients were treated by specialists A and B, respectively). We compared the actually prescribed regimens with those selected by the system using the same available information. The overall exact agreement was 82.9% (29 out of 35), with the exact agreement with specialists A and B both at 100%. The exact agreement for the remaining 11 physicians not involved in the system training was 73.9% (17 out of 23), an impressive result given the fact that expert opinion can be quite divergent for treatment decisions of such complexity. Our specialists also carefully examined the six mismatched cases and deemed that the system actually chose a more appropriate regimen for four of them. In the other two cases, either would be reasonable choices. Our approach has the capabilities of generalizing, learning, and representing knowledge even in the face of weak consensus, and being readily upgradeable to new medical knowledge. These are practically important features to medical applications in general, and HIV/AIDS treatment in particular, as national HIV/AIDS treatment guidelines are modified several times per year.
Ying
Hao
H
Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA. hao.ying@wayne.edu
Lin
Feng
F
MacArthur
Rodger D
RD
Cohn
Jonathan A
JA
Barth-Jones
Daniel C
DC
Ye
Hong
H
Crane
Lawrence R
LR
D019380:Anti-HIV Agents / Q000008:administration & dosage*; D020000:Decision Support Systems, Clinical; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D017143:Fuzzy Logic; D015658:HIV Infections / Q000175:diagnosis / Q000188:drug therapy*; D006801:Humans; D011786:Quality Control; D012815:Signal Processing, Computer-Assisted; D016896:Treatment Outcome
D019380:Anti-HIV Agents