Mental health problems including depressive disorders, anxiety and related disorders are becoming more prevalent, entailing a huge health burden, worldwide. Managing mental health problems depends on the patients’ ability to report their cognitive and emotional states, their perceptions on state progression, and the impact on their social relationships. Individual’s skills, capabilities, and temperament could impact on his/her ability to communicate perceptions, making clinical practice difficult to healthcare professionals. Additionally, the variability of patients’ responses might impede ideal clinical practice following the principles of evidence-based medicine. Hence, a trial-and-error strategy searching for the best therapeutic alternative is often required. This fact might impact negatively in the patient’s trust in the selected therapy which may result in poor outcomes and health complications.
In this context, the use of artificial intelligence in psychiatry has risen over the past years to meet the growing need for improved mental health care. These smart technologies enable the provision of tools that support in the diagnosis, symptom tracking, disease course prediction, and psychoeducation of patients with mental health problems. The use of these tools may overcome the trial-and-error-driven mental health care by supporting precise diagnoses and prognoses, improved decision-making, and personalized medicine.
This special track invites original research on innovative concepts, models, algorithms, methods, tools, and services that address challenges at the management of patients with mental health problems through smart technologies. These may include technological issues around data registration, data privacy, security, standards, and taxonomies.


Topics of interest include, but are not limited to:
● Analysis of social media for mental health risk evaluation, detection or monitoring
● Designing, development, and testing of chatbots for mental health
● Sentiment analysis in mental health research
● Process mining techniques for human behavioral modelling in mental health
● Design, development, and evaluation of interactive AI solutions for mental health
● Integrated monitoring for mental health management
● Indirect monitoring to assess mental health status
● Ethics and health equity in the use of AI in mental health research

Important Dates

The special track will take place in parallel with the general conference track. Submission deadlines are reported here.

Submission Guidelines

Please refer to this page.

Special Issue

Available Soon


Vicente Traver

SABIEN Research Group, ITACA institute, Universitat
Politécnica Valéncia, Spain

Kerstin Denecke

Bern University of Applied Sciences, Institute for Medical
Informatics, Bern, Switzerland

Program Committee

Carlos Fernández Llatas

SABIEN Research Group, ITACA institute, Universitat
Politécnica Valéncia, Spain

Octavio Rivera Romero

Universidad de
Sevilla, Spain

Antonio Martínez-Millana

SABIEN Research Group, ITACA institute,
Universitat Politécnica Valéncia, Spain