The digital transformation in the prevention and management of suicide risk in adult patients through the use of tools with AI algorithms, trained through clinical evidence.

Welcome to IDICIUS

The IDICIUS project aims to develop and validate the use of a digital platform to monitor and predict suicide risk in mental health patients.

The project is aimed at generating new scientific knowledge in the field of mental health and suicidal behaviour. This knowledge is essential to promote the digitization of the healthcare system, incorporating evidence-based and validated tools for suicide risk prevention and patient follow-up.

The analysis and exploitation of data from different data sources, one of the major challenges facing the digital transition in healthcare, will be carried out together with the use of technological advances in Artificial Intelligence (AI), medicine preventive and personalized with early detection using Machine Learning (ML), which are offering great opportunities to generate value from available healthcare datasets in an effective and efficient manner. These technologies also offer the opportunity for patient-centered research by designing, for the first time, specialized, evidence-validated smart digital tools and thus respond to their needs, offering self-management and personalized recommendations.


Projects of Ecological Transition and Digital Transition 2021. File No

Duration of the project: 2 years

Project IP in the I3PT: Dr. Diego Palao, Mental Health Service of the Parc Taulí University Hospital, coordinator of the Mental Health and Neurosciences research area of ​​the I3PT, and head of the CIBERSAM group.

Economic endowment: 241.500 €


  • The use of digital tools based on predictive models trained with AI techniques would help predict and detect cases with the highest risk of suicide attempt and allow the specific development of personalized treatments and interventions to reduce the risk.
  • The use of digital tools in mental health based on the analysis of the relationship between death by suicide and clinical, biological and socio-demographic factors through Real World Data (RWD) from electronic medical record records would improve efficiency and effectiveness in early detection and personalized management of high-risk cases. This would allow the development of proactive intervention systems that would allow the adaptation and determination of the type and intensity of resources needed for each clinical situation of suicide risk.

Objectives and methodology

IDICIUS pursues two main objectives:

  1. Design a digital tool based on data-driven predictive models that help prevent suicide risk by analyzing the relationship between suicide attempts and/or ideation, and clinical, biological and socio-demographic factors, through Real World Date (RWD) of records of the electronic clinical record (HCE).
  2. Design a digital tool based on predictive models to prevent the risk of suicide by analyzing the relationship between death by suicide and clinical, biological and socio-demographic factors through RWD of the HCE.

IDICIUS will consist of carrying out a retrospective study based on data recorded in the HCE, through the application of AI and machine learning algorithms trained using clinical data. The study will integrate data from different sources:

  1. HCIS electronic register of patients undergoing treatment at the Mental Health Service of the Parc Taulí University Hospital, with a reference population of 1 million people.
  2. The registry of the Suicide Risk Code of Catalonia (CSR-cat) which incorporates clinical and socio-demographic information of cases with attempted suicide and/or suicidal ideation considered to be of greater risk.
  3. The suicide registry of the Institute of Legal Medicine and Forensic Sciences (IMLCF).
  4. The database of treatments and drugs prescribed in external consultations of the Catalan Health Service.
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