A comparison of two computer-based prognostic systems for AIDS.
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.
Ohno-Machado
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Section on Medical Informatics, Stanford University School of Medicine, CA 94305, USA.
Musen
M A
MA
D000163:Acquired Immunodeficiency Syndrome / Q000401:mortality*; D003198:Computer Simulation; D018450:Disease Progression; D015658:HIV Infections / Q000503:physiopathology; D006801:Humans; D016571:Neural Networks, Computer; D011379:Prognosis; D016016:Proportional Hazards Models; D016019:Survival Analysis