A multidisciplinary research project combining Data Science and Law to identify behavioral patterns and prevent the use of cannabis, alcohol, and tobacco among people with disabilities. The initiative is supported by the proprietary software MOISES, which is specifically designed to support decision-making in the management of social services.
Early Detection of Addictions using Data Science and Legal Analysis
Official Name of Signatory
Generalitat Valenciana, Spain
Delegation
Europe
Website of the Signatory
Name of the person presenting the Good Practice
Helena Bonet Jaén
Position/Job Title of person presenting the Good Practice
Associate Professor Miguel Hernández University of Elche (UMH)
Aim of the Good Practice
To apply data science techniques to analyze socio-health data and detect correlations that anticipate the development of addictions. Additionally, it aims to address crucial legal questions -such as informed consent and personal autonomy limits- to ensure good research practices and develop proposals for legislative reform that improve the protection of this vulnerable group.
Target Group of the Good Practice
Older Person / Disabilities
Annual Monitoring Report
2026
Consistency over time
The project is defined as a "long-term project"
Evaluation of the Good Practice
Initially, it will be evaluated through a review of existing AI and ICT methodologies for the early detection of cannabis use.
Key stakeholders and partnerships
Governments (national, regional, local), Public administration, Social protection system
Future Goal 1
Develop indicators and practical recommendations that enable social and healthcare professionals to identify risk patterns leading to addiction
Future Goal 2
Develop proposals for legislative reform that improve the protection of people with disabilities and materialize the research results into high-impact scientific publications.