home
***
CD-ROM
|
disk
|
FTP
|
other
***
search
/
The Arsenal Files 6
/
The Arsenal Files 6 (Arsenal Computer).ISO
/
health
/
med9605a.zip
/
M9650264.TXT
< prev
next >
Wrap
Text File
|
1996-03-09
|
3KB
|
41 lines
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).