Jose L Ayala

Full Professor

Universidad Complutense de Madrid

Spain

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Jose L Ayala is a Full Professor at Universidad Complutense de Madrid, Spain. His research focuses on women's health, cardiovascular disease, and machine learning applications in healthcare. Recent articles include studies on early-onset preeclampsia, stroke detection, and brain metabolism in neurodegenerative disorders.

Articles (20)

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Collaborators (7)

María Díez-Cirarda

Universidad Complutense de Madrid

SPAIN

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Cristina Rodriguez-Antona

Investigador Científico/ Head

Instituto de Investigaciones Biomédicas Alberto Sols

SPAIN

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María González-Nosti

Profesor Contratado Doctor

University of Oviedo

SPAIN

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Jordi A. Matias-Guiu

Universidad Complutense de Madrid

SPAIN

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José Manuel Moya Fernández

Universidad Politécnica de Madrid

SPAIN

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José Luis Risco Martín

Professor

Universidad Complutense de Madrid

SPAIN

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Josue Pagan

Universidad Politécnica de Madrid

SPAIN

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Jose L Ayala

Research Interests

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About

Articles (20)

Graph-Based Immune Checkpoint Inhibitor Response Prediction Model for Metastatic Renal Cell Carcinoma Patients

Cardiovascular disease in women with early-onset preeclampsia: a matched case-control study

Comprehensive Approach with Machine Learning Techniques to Investigate Early-Onset Preeclampsia and Its Long-Term Cardiovascular Implications

Early stroke detection through machine learning in the prehospital setting

Predictive modeling of complications arising from early-onset preeclampsia in pregnant women

Autoencoder techniques for survival analysis on renal cell carcinoma

Women's health: an imAging-based cardiovascular risK-rEdUction Program (WAKE UP) study. Rationale and design

Effects of single-session repetitive transcranial magnetic stimulation to identify the optimal brain target in primary progressive aphasia

Data-driven prediction of regional brain metabolism using neuropsychological assessment in Alzheimer's disease and behavioral variant Frontotemporal dementia

Unraveling brain fog in <scp>post‐COVID</scp> syndrome: Relationship between subjective cognitive complaints and cognitive function, fatigue, and neuropsychiatric symptoms

Development of criteria for cognitive dysfunction in post-COVID syndrome: the IC-CoDi-COVID approach

Neuropsychological Predictors of Fatigue in Post-COVID Syndrome

Body Complexion and Circulating Lipids in the Risk of TDP-43 Related Disorders

Cluster-Then-Classify Methodology for the Identification of Pain Episodes in Chronic Diseases

Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms

Efficient micro data centres deployment for mobile healthcare monitoring systems in IoT urban scenarios

Energy-aware task scheduling in data centers using an application signature

Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging

Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study

Fast energy estimation framework for long-running applications

Collaborators (7)

María Díez-Cirarda

Cristina Rodriguez-Antona

María González-Nosti

Jordi A. Matias-Guiu

José Manuel Moya Fernández

José Luis Risco Martín

Josue Pagan

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