Artificial Intelligent Systems Based on Supervised HUMANN for Differential Diagnosis of Cognitive Impairment: Towards a 4P-HCDS

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.

[1]  Paul R. Solomon,et al.  Should we screen for Alzheimer's disease? , 2005 .

[2]  Tariq Samad,et al.  Self–organization with partial data , 1992 .

[3]  Soo Borson,et al.  Simplifying Detection of Cognitive Impairment: Comparison of the Mini‐Cog and Mini‐Mental State Examination in a Multiethnic Sample , 2005, Journal of the American Geriatrics Society.

[4]  Secundino López Pousa,et al.  La demencia: concepto y epidemiología , 2006 .

[5]  Carmen Paz Suárez Araujo,et al.  An Ensemble Approach for the Diagnosis of Cognitive Decline with Missing Data , 2008, HAIS.

[6]  Carmen Paz Suárez Araujo,et al.  A Parametric Study of Humann in Relation to the Noise: Application for the Identification of Compounds of Environmental Interest , 2003 .

[7]  Carmen Paz Suárez Araujo,et al.  Automatic Prognostic Determination and Evolution of Cognitive Decline Using Artificial Neural Networks , 2007, IDEAL.

[8]  Robi Polikar,et al.  Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer's disease , 2007, Comput. Biol. Medicine.

[9]  I. McKeith,et al.  Pathological correlates of late-onset dementia in a multicentre, community-based population in England and Wales , 2001, The Lancet.

[10]  Yang Li,et al.  Analysis of Tiling Microarray Data by Learning Vector Quantization and Relevance Learning , 2007, IDEAL.

[11]  C. P. Suárez Araujo,et al.  Clinical web environment to assist the diagnosis of Alzheimer's disease and other dementias , 2004 .

[12]  Christoph Lehmann,et al.  Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG) , 2007, Journal of Neuroscience Methods.

[13]  P. Scheltens,et al.  Advances in the early detection of Alzheimer's disease , 2004, Nature Reviews Neuroscience.

[14]  Tomas Hrycej,et al.  Modular Learning in Neural Networks: A Modularized Approach to Neural Network Classification , 1992 .

[15]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .

[16]  E. Bernabé,et al.  Health and Quality of Life Outcomes , 2004 .

[17]  Tomas Hrycej.  Modular learning in neural networks - a modularized approach to neural network classification , 1992, Sixth-Generation computer technology series.

[18]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.