Register-based research innovations to address the youth mental health crises

The rising prevalence of youth mental health disorders has outpaced the capacity of existing services.

To address this, we propose register-based research innovations to support prevention at the societal level and improve individualized treatment in primary and specialist care.

We integrate national Registers with novel data from the general population, primary, and specialist care, combined with recent advances in large language models (LLMs) and causal machine learning.

Our interdisciplinary team of epidemiologists, health care providers and AI computer scientists aim to advance the use of LLMs in register-based research, by developing a language model architecture for unstructured medical records and prescription data, which will be implemented in three specific aims:1) Identify emerging risk factors to guide prevention using causal inference and longitudinal survey data linked with national registers 2) Strengthen youth mental health services by applying new data and methods to improve patient stratification in primary care3) Optimize treatment in specialist youth mental health services, by better understanding individualized treatment effects based on patient characteristics This research will improve youth mental health, develop innovative methods for register-based research, including a large language model to handle unstructured data, and train the next generation of register-based researchers to address healthcare-relevant questions using cutting-edge AI methods.