Antonio Artés

Professor

Universidad Carlos III de Madrid

Spain

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Antonio Artés is a Professor at Universidad Carlos III de Madrid, Spain. His research areas encompass digital mental health, artificial intelligence applications in scientific writing, and innovative methodologies in predictive analytics for suicide risk assessment. He has conducted significant studies on the integration of digital safety measures in healthcare and the utilization of deep learning techniques for data analysis in medical contexts.

Articles (15)

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2025

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2021

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2021

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2021

Collaborators (7)

Rodrigo Carmona Camacho

Associate Professor

Universidad Complutense de Madrid

SPAIN

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Pablo Martínez Olmos

Profesor Titular (associate professor)

Universidad Carlos III de Madrid

SPAIN

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Jorge Lopez-Castroman

Professor titular

Universidade de Santiago de Compostela

SPAIN

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Philippe Courtet

Head of Department

FRANCE

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Ignacio Peis

Technical University of Denmark

DENMARK

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Maria Luisa Barrigon

Associate Professor

Universidad Complutense de Madrid

SPAIN

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David Ramírez

Associate Professor

Universidad Carlos III de Madrid

SPAIN

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Antonio Artés

Research Interests

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Articles (15)

Impact of Digital Safety Plan Activation on Subsequent Emergency Departments Visits Following an Initial Suicide Attempt: Quasi-Experimental Study

Emotion Forecasting: A Transformer-Based Approach

Reflections on the Potential and Risks of AI for Scientific Article Writing after the AI Endorsement by Some Scientific Publishers: Focusing on Scopus AI

The growing need to integrate digital mental health into psychiatric and medical education

One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study

Unsupervised learning of global factors in deep generative models

Deep autoregressive models with spectral attention

Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study

Medical Data Wrangling With Sequential Variational Autoencoders

Change-point detection in hierarchical circadian models

Multinomial Sampling of Latent Variables for Hierarchical Change-Point Detection

Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning–Based Ecological Momentary Assessment Study

Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients

Use of Ecological Momentary Assessment Through a Passive Smartphone-Based App (eB2) by Patients With Schizophrenia: Acceptability Study

Disturbed sleep as a clinical marker of wish to die: A smartphone monitoring study over three months of observation

Collaborators (7)

Rodrigo Carmona Camacho

Pablo Martínez Olmos

Jorge Lopez-Castroman

Philippe Courtet

Ignacio Peis

Maria Luisa Barrigon

David Ramírez

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