Modular Learning in Neural Networks: A Modularized Approach to Neural Network Classification
Modular Learning in Neural Networks: A Modularized Approach to Neural Network Classification
Systems Analysis Modelling Simulation - Special issue: Intelligent systems, models and databases for environmental research
An Ensemble Approach for the Diagnosis of Cognitive Decline with Missing Data
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part II
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Differential and early diagnosis of cognitive impairment (CI) continues being one of the crucial points to which clinical medicine faces at every level of attention, and a significant public health concern. This work proposes new CI diagnostic tools based on a data fusion scheme, artificial neural networks and ensemble systems. Concretely we have designed a supervised HUMANN [1] with capacity of missing data processing (HUMANN-S) and a HUMANN-S ensemble system. These intelligent diagnostic systems are inside EDEVITALZH, a clinical virtual environment to assist the diagnosis and prognosis of CI, Alzheimer's disease and other dementias. Our proposal is a personalized, predictive, preventive, and participatory-healthcare delivery system (4P-HCDS) and is an optimal solution for an e-health framework. We explore their ability presenting preliminary results on differential diagnosis of CI using neuropsychological tests from 267 consultations on 30 patients by the Alzheimer's Patient Association of Gran Canaria.