An individual blood glucose control model (IBGCM) based on the Mixture of Experts (MOE) neural networks algorithm was designed
to improve the diabetic care. MOE was first time used to integrate multiple individual factors to give suitable decision advice
for diabetic therapy. The principle of MOE, design and implementation of IBGCM were described in details. The blood glucose
value (BGV) from IBGCM extremely approximated to training data (r=0.97± 0.05, n=14) and blood glucose control aim (r=0.95± 0.06, n=7).
[Show abstract][Hide abstract] ABSTRACT: Artificial neural networks are suggested for use in predicting metal ion concentration in human blood plasma. Simulated and available experimental data are used to train the artificial neural network. Particularly, using 850 simulated samples, the network predicted the magnesium-free ion concentration with an average error smaller than 1%. Clinical data recently reported for 20 patients were considered and the artificial neural network predicted the concentration of free magnesium ion with an average error of about 6%. Overall, the approach of using artificial neural networks as an alternative or complementary strategy to deal with the analysis of human blood plasma can be useful for clinical diagnostics, if there is sufficient data to train the artificial neural network.
Clinical Chemistry and Laboratory Medicine 02/2005; 43(9):939-46. DOI:10.1515/CCLM.2005.161 · 2.71 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: An improved nonlinear mixture of experts model (ME) provides a modular approach wherein component neural networks are made specialists on subparts of a problem. This paper studied the application of improved ME variants to multivariate nonlinear systems of clinical decision problems, which are known to be difficult to be dealt with. The aim is to develop a new operation quantity planning decision model (OQPDM) based on improved nonlinear mixture of expert neural networks to predict the corrective quantity of lateral and medial rectus in strabotomy to instruct and improve practice. The corrective rate of strabotomy from OQPDM (97%) is better than past experience (76%) and shows effective prediction. OQPDM with improved nonlinear ME can offer robustness for potential application in other clinical decision support system, which can be implemented to develop a embeddable system for minisized instrument for eye checking
Machine Learning and Cybernetics, 2006 International Conference on; 09/2006
[Show abstract][Hide abstract] ABSTRACT: The ensemble method called Mixture of Experts, based on the Divide-and-Conquer principle was proposed to model a new Strabotomy Surgical Decision system, in which an extra gating component is used to compute weights dynamically according to the inputs. The aim is to develop a new operation quantity planning decision model (OQPDM) to predict the corrective quantity of lateral and medial rectus in strabotomy to instruct practice. The corrective rate of strabotomy from OQPDM (98.4%) is better than past experience (81.6%) and shows effective prediction. improved OQPDM can offer robustness for potential application in other clinical decision support system, which can develop a system for minisized instrument of eye checking and operation planning.
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