Sequential use of neural networks for survival prediction in AIDS.
Prognostic assessment of patients is a key part of medical care. Although neural networks can be used to model survival, their accuracy has been limited for a variety of factors, including (1) the lack of data balance in certain intervals and (2) the lack of representation of temporal dependencies in the network architecture. Both problems can be solved with the use of sequential neural networks, which establish predictions for a certain time point and then use these predictions to produce survival estimates for other time points. If the sequence of models is adequate, sequential neural networks produce more accurate estimates of survival than standard neural networks, as shown in this example in the domain of AIDS. Assessments of survival in one, two, three, five and six years become more accurate (as measured by the areas under the ROC curves) when initial predictions of survival in four years are used in a sequential neural network model.
Ohno-Machado
L
L
Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. machado@dsg.harvard.edu
D000163:Acquired Immunodeficiency Syndrome; D019540:Area Under Curve; D018450:Disease Progression; D006801:Humans; D016571:Neural Networks, Computer; D011379:Prognosis; D012372:ROC Curve; D012680:Sensitivity and Specificity; D016019:Survival Analysis