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Machine learning model (ELSA) for the identification and prediction of psychological stress: A validation study.

Machine learning model (ELSA) for the identification and prediction of psychological stress: A validation study.

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
DRKS
Registry ID
DRKS00036444
Enrollment
10000
Registered
2025-06-03
Start date
2025-09-01
Completion date
Unknown
Last updated
2025-10-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

GAD-7 scores PHQ-9 scores SBQ-R scores PSQ scores LEC-5 scores

Interventions

Group 1: The research project described here is designed as a multicenter study. All partner universities will receive the English translation of the protocol approved by the Ethics Committee of the F

Sponsors

Institut für Psychiatrische Phänomik und Genomik
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: currently studying

Exclusion criteria

Exclusion criteria: age under 18 years

Design outcomes

Primary

MeasureTime frame
The primary aim of the project is to validate the detection accuracy of the ELSA model in different countries and cohorts.

Secondary

MeasureTime frame
The secondary aim is to investigate the relationship and predictability between substance use, traumatic experiences, suicidality, psychotic symptoms, psychological resilience, and non-psychiatric variables assessed by the ELSA model. In addition, the results in higher-income countries will be compared with those in low- and middle-income countries.

Countries

Ethiopia, Germany

Contacts

Public ContactKristina Adorjan

LMU Department of Psychiatry and Psychotherapy

Kristina.Adorjan@med.uni-muenchen.de+49 89 4400 55546

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 4, 2026