A fuzzy discrete event system approach to determining optimal HIV/AIDS treatment regimens

Hao Ying, Feng Lin, Rodger David MacArthur, Jonathan A. Cohn, Daniel C. Barth-Jones, Hong Ye, Lawrence R. Crane

Research output: Contribution to journalArticle

44 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)663-676
Number of pages14
JournalIEEE Transactions on Information Technology in Biomedicine
Volume10
Issue number4
DOIs
StatePublished - Oct 1 2006

Fingerprint

Discrete event simulation
Viruses
Acquired Immunodeficiency Syndrome
Decision making
HIV
Decision Making
Physicians
System theory
Finite automata
Medical applications
Therapeutics
Consensus
Classifiers
Genetic algorithms
Learning
Systems Theory
Highly Active Antiretroviral Therapy
Expert Testimony
Uncertainty
Retrospective Studies

Keywords

  • Decision-making
  • Discrete event systems
  • Fuzzy logic
  • Human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) treatment

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

A fuzzy discrete event system approach to determining optimal HIV/AIDS treatment regimens. / Ying, Hao; Lin, Feng; MacArthur, Rodger David; Cohn, Jonathan A.; Barth-Jones, Daniel C.; Ye, Hong; Crane, Lawrence R.

In: IEEE Transactions on Information Technology in Biomedicine, Vol. 10, No. 4, 01.10.2006, p. 663-676.

Research output: Contribution to journalArticle

Ying, Hao ; Lin, Feng ; MacArthur, Rodger David ; Cohn, Jonathan A. ; Barth-Jones, Daniel C. ; Ye, Hong ; Crane, Lawrence R. / A fuzzy discrete event system approach to determining optimal HIV/AIDS treatment regimens. In: IEEE Transactions on Information Technology in Biomedicine. 2006 ; Vol. 10, No. 4. pp. 663-676.
@article{65adc45b26734104ada119eaf4eb6e97,
title = "A fuzzy discrete event system approach to determining optimal HIV/AIDS treatment regimens",
abstract = "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.",
keywords = "Decision-making, Discrete event systems, Fuzzy logic, Human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) treatment",
author = "Hao Ying and Feng Lin and MacArthur, {Rodger David} and Cohn, {Jonathan A.} and Barth-Jones, {Daniel C.} and Hong Ye and Crane, {Lawrence R.}",
year = "2006",
month = "10",
day = "1",
doi = "10.1109/TITB.2006.874200",
language = "English (US)",
volume = "10",
pages = "663--676",
journal = "IEEE Journal of Biomedical and Health Informatics",
issn = "2168-2194",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

TY - JOUR

T1 - A fuzzy discrete event system approach to determining optimal HIV/AIDS treatment regimens

AU - Ying, Hao

AU - Lin, Feng

AU - MacArthur, Rodger David

AU - Cohn, Jonathan A.

AU - Barth-Jones, Daniel C.

AU - Ye, Hong

AU - Crane, Lawrence R.

PY - 2006/10/1

Y1 - 2006/10/1

N2 - 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.

AB - 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.

KW - Decision-making

KW - Discrete event systems

KW - Fuzzy logic

KW - Human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) treatment

UR - http://www.scopus.com/inward/record.url?scp=33750200787&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33750200787&partnerID=8YFLogxK

U2 - 10.1109/TITB.2006.874200

DO - 10.1109/TITB.2006.874200

M3 - Article

VL - 10

SP - 663

EP - 676

JO - IEEE Journal of Biomedical and Health Informatics

JF - IEEE Journal of Biomedical and Health Informatics

SN - 2168-2194

IS - 4

ER -