The goal of this study was to compare standard and sequential @entity1274 models for recognition of patterns of disease progression. Medical researchers who perform prognostic modeling usually oversimplify the problem by choosing a single point in time to predict outcomes (e.g. death in 5 years). This approach not only fails to differentiate patterns of disease progression, but also wastes important information that is usually available in time-oriented research data bases. The adequate use of sequential neural networks can improve the performance of prognostic systems if the interdependencies among prognoses at different intervals of time are explicitly modeled. In such models, predictions for a certain interval of time (e.g. death within 1 year) are influenced by predictions made for other intervals, and prognostic survival curves that provide consistent estimates for several points in time can be produced. We developed a system of @entity1274 models that makes use of time-oriented data to predict development of @entity301 ( @entity301 ), using a set of 2594 @entity1 . The output of the @entity1274 system was a prognostic curve representing survival without @entity301 , and the inputs were the values of demographic, clinical, and laboratory variables. The system of neural networks was trained by backpropagation and its results were evaluated in test sets of previously unseen cases. We showed that, by explicitly modeling time in the @entity1274 architecture, the performance of the prognostic index, measured by the area under the receiver operating characteristic (ROC) curve, was significantly improved (p < 0.05).

Sequential versus standard neural networks for pattern recognition: an example using the domain of XXXX .

@entity1 :: ('9606', 'Species') :: ['patients'] @entity1274 :: ('MESH:D006319', 'Disease') :: ['neural network'] @entity301 :: ('MESH:D003327', 'Disease') :: ['coronary heart disease', 'CHD']
@entity301 :: (MESH:D003327,Disease) :: ['coronary heart disease']
