Person:
Ayala Rodrigo, José Luis

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First Name
José Luis
Last Name
Ayala Rodrigo
URI
https://hdl.handle.net/20.500.14352/78677
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Informática
Department
Arquitectura de Computadores y Automática
Area
Arquitectura y Tecnología de Computadores
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2022 - 20232
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Start date: 2012 × Subject: 616.894-053.9 ×

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Now showing 1 - 2 of 2
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    A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers
    (CNS Neuroscience and Therapeutics, 2023) Hernández Lorenzo, Laura; Gil‐Moreno, Maria José; Ortega‐Madueño, Isabel; Cárdenas Fernández, María Cruz; Diez‐Cirarda, María; Delgado Álvarez, Alfonso; Palacios‐Sarmiento, Marta; Matías-Guiu Guía, Jorge; Corrochano Sánchez, Silvia; Ayala Rodrigo, José Luis; Matias‐Guiu Antem, Jordi
    Aims: The AT(N) classification system not only improved the biological characteriza-tion of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values. Methods: We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aβ(1– 42), Aβ(1– 42 ) /Aβ(1– 40) ratio, tTau, and pTau. Results: The optimal solution yielded three clusters in both cohorts, significantly dif-ferent in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impair-ment subjects with faster progression to dementia. Conclusion: We propose this data-driven three- group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, com-plementary to the AT(N) system classification.
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    Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning‐aided neuropsychological assessment using feature engineering and genetic algorithms
    (International Journal of Geriatric Psychiatry, 2022) García‐Gutiérrez, Fernando; Delgado Álvarez, Alfonso; Delgado‐Alonso, Cristina; Díaz‐Álvarez, Josefa; Pytel Córdoba, Vanesa; Valles‐Salgado, María; Gil Idoate, María Jose; Hernández Lorenzo, Laura; Matías-Guiu Guía, Jorge; Ayala Rodrigo, José Luis; Matias-Guiu Antem, Jordi
    Background Neuropsychological assessment is considered a valid tool in the diagnosis of neurodegenerative disorders. However, there is an important overlap in cognitive profiles between Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD), and the usefulness in diagnosis is uncertain. We aimed to develop machine learning-based models for the diagnosis using cognitive tests. Methods Three hundred and twenty-nine participants (170 AD, 72 bvFTD, 87 healthy control [HC]) were enrolled. Evolutionary algorithms, inspired by the process of natural selection, were applied for both mono-objective and multi-objective classification and feature selection. Classical algorithms (NativeBayes, Support Vector Machines, among others) were also used, and a meta-model strategy. Results Accuracies for the diagnosis of AD, bvFTD and the differential diagnosis between them were higher than 84%. Algorithms were able to significantly reduce the number of tests and scores needed. Free and Cued Selective Reminding Test, verbal fluency and Addenbrooke's Cognitive Examination were amongst the most meaningful tests. Conclusions Our study found high levels of accuracy for diagnosis using exclusively neuropsychological tests, which supports the usefulness of cognitive assessment in diagnosis. Machine learning may have a role in improving the interpretation and test selection.