Measuring the plasma parathyroid hormone (PTH) concentration is crucial to evaluate renal bone disease in patients with renal failure. Although frequent measurement is needed to avoid inadequate prescription of phosphate binders and vitamin D preparations, artificial intelligence can repeatedly perform the forecasting tasks and may be a satisfactory substitute for laboratory tests. Neuro-fuzzy technology represents a promising forecasting application in clinical medicine. We therefore constructed a coactive neuro-fuzzy inference system (CANFIS) to predict plasma PTH concentrations in hemodialysis patients. The CANFIS was constructed with clinical parameters (patient age, plasma albumin, calcium, phosphorus, alkaline phosphatase, and calcium-phosphorus product) from a cohort of hemodialysis patients, and plasma PTH concentration measured by radioimmunoassay (RIA) was the supervised outcome. The accuracy of the CANFIS was prospectively compared with RIA in another hospital. Plasma PTH concentrations measured by RIA and predicted by CANFIS were 179.04 +/- 38.18 ng/l and 179.34 +/- 37.76 ng/l, respectively (p = 0.15). The CANFIS was able to precisely estimate plasma PTH concentrations in hemodialysis patients. These results suggest that the neuro-fuzzy technology, based on limited clinical parameters, is an excellent alternative to RIA for accurately predicting plasma PTH concentration in hemodialysis patients.
Aged Algorithms