The digital transformation in primary care and specialized mental health centers through artificial intelligent/machine learning tools for the prevention and monitoring of patients at risk of suicide in Catalonia

Research project description

PhD will investigate the digital transition in mental health care by preventing suicidal behavior in different population groups. 

Suicide is currently the leading external cause of mortality in Europe and one of the main causes of premature death. The study of factors that may contribute to the detection of individuals at higher risk and contribute to a more effective suicide prevention. However, this is not enough to be able to predict suicidal behavior on an individual basis under the conditions of real clinical practice. Moreover, it is necessary to take into account the different population groups (adults, children, adolescents) and different clinical settings (primary care and mental health specialty settings). Primary care has an important role in suicide prevention, knowing that among people who die by suicide, 83% have visited at primary care in the prior year, and 50% have visited that provider within 30 days of their death, rather than a psychiatrist. Furthermore, it is well known risk factors for suicide are already available in the routine electronic health records (EHRs).  

This is a retrospective, population-based study using the identified structured EHRs from Catalonia (Spain). We will analyze data from the electronic health records of Catalonia, an area with 7.5 million people in northeast Spain. The Catalan Health Service (CHS) provides public, universal healthcare to the entire population of Catalonia. We will review date of the period from 2018 to 2023. The objective of the present project is design a digital tool based on machine learning/artificial intelligence (ML/AI) predictive models that could prevent the suicide risk.

The estimation duration is three years and the PhD student has to be involved in all phases of the study, including: i) extraction and integration of data following a validated clinical methodology, ii) predictive models development, adjustment and validation, iii) design and integrate a care platform design, iv) design and plan feasibility, clinical effectiveness and implementation assessment, and v) communication and scientific dissemination of results. 

Academic background / Skills

Candidates must hold a degree that allows admission to the official doctoral programme at UAB.  

Additional requirements for a stronger application are: 

The pre-doctoral research associate role for the suicide prevention AI project requires a degree in psychology or a related field, along with a foundational understanding of biostatistics, artificial intelligence, machine learning and data science.

Candidates should combine a strong foundation in mental health with a keen interest in AI and related disciplines.

Strong analytical, research, and communication skills are valued for effective collaboration and data analysis in this role.  

Research group/s description

The research group “e-Mental Health, prevention and epidemiology of neuropsychiatric diseases” of the Institute for Research and Innovation Parc Taulí (I3PT), made up of 34 Parc Taulí professionals (18 PhDs), has developed in the last three years a successful strategy of growth and consolidation of excellence research in the area of neurosciences driven by an ethical imperative.

The group has been able to attract talent and to form strong alliances with the INc (linked group CIBERSAM, Unitat Mixta UAB Neurociència Traslacional).  

Since its origins, our group shows a sum of accumulated citations of more than 2230, and an h-index of 21. In the last 4 years the group has published 138 articles in Q1 scientific journals, of which 32 are D1. The lines of epidemiology and prevention of depression and suicidal behaviors, e-Mental Health and prevention and therapeutic evaluation of stroke and other diseases with cognitive impairment have been consolidated and have ambitious projects.