Document 0264
 DOCN  M9650264
 TI    A comparison of two computer-based prognostic systems for AIDS.
 DT    9605
 AU    Ohno-Machado L; Musen MA; Section on Medical Informatics, Stanford
       University School of; Medicine, CA 94305, USA.
 SO    Proc Annu Symp Comput Appl Med Care. 1995;:737-41. Unique Identifier :
       AIDSLINE MED/96123820
 AB    We compare the performances of a Cox model and a neural network model
       that are used as prognostic tools for a cohort of people living with
       AIDS. We modeled disease progression for patients who had AIDS
       (according to the 1993 CDC definition) in a cohort of 588 patients in
       California, using data from the ATHOS project. We divided the study
       population into 10 training and 10 test sets and evaluated the
       prognostic accuracy of a Cox proportional hazards model and of a neural
       network model by determining the number of predicted deaths, the
       sensitivities, specificities, positive predictive values, and negative
       predictive values for intervals of one year following the diagnosis of
       AIDS. For the Cox model, we further tested the agreement between a
       series of binary observations, representing death in one, two, and three
       years, and a set of estimates which define the probability of survival
       for those intervals. Both models were able to provide accurate numbers
       on how many patients were likely to die at each interval, and reasonable
       individualized estimates for the two- and three-year survival of a given
       patient, but failed to provide reliable predictions for the first year
       after diagnosis. There was no evidence that the Cox model performed
       better than did the neural network model or vice-versa, but the former
       method had the advantage of providing some insight on which variables
       were most influential for prognosis. Nevertheless, it is likely that the
       assumptions required by the Cox model may not be satisfied in all data
       sets, justifying the use of neural networks in certain cases.
 DE    Acquired Immunodeficiency Syndrome/*MORTALITY  Comparative Study
       *Computer Simulation  Disease Progression  Human  HIV
       Infections/PHYSIOPATHOLOGY  *Neural Networks (Computer)  Prognosis
       *Proportional Hazards Models  Support, Non-U.S. Gov't  Support, U.S.
       Gov't, P.H.S.  Survival Analysis  JOURNAL ARTICLE

       SOURCE: National Library of Medicine.  NOTICE: This material may be
       protected by Copyright Law (Title 17, U.S.Code).