Usefulness of artificial neural networks to predict follow-up dietary protein intake in hemodialysis patients.

Usefulness of artificial neural networks to predict follow-up dietary protein intake in hemodialysis patients. Background Artificial neural networks (ANN) represent a promising alternative to classical statistical and mathematic methods to solve multidimensional nonlinear problems. The aim of the study was to verify, by comparing the performance of ANN with that of experienced nephrologists, whether ANN are useful tools in hemodialysis to predict the follow-up (=1 month after the observation used for the prediction) dietary protein intake (PCR), and whether their performance is influenced by the size of the population and by the data pool used to built the model. Methods A combined retrospective and prospective observational study was performed in two Swiss dialysis units (84 chronic hemodialysis patients, 500 monthly clinical observations and biochemical test results). Using mathematical models based on linear regressions to evaluate the variables, ANN were built and then prospectively and interinstitutionally compared with the ability of six experienced nephrologists to predict the follow-up PCR. Results ANN compared with nephrologists gave a more accurate correlation between estimated and calculated follow-up PCR ( P Conclusion The use of ANN significantly improves the ability of the experienced nephrologist to estimate and to detect an unsatisfactory (