Abstract summary 

This research project aims to build a Machine Learning algorithm (ML) to predict first-time ADHD diagnosis, given that it is the most frequent mental disorder for the non-adult population.We used a stacked model combining 4 ML approaches to predict the presence of ADHD. The dataset contains data from population health care administrative registers in Catalonia comprising 1,225,406 non-adult individuals for 2013-2017, linked to socioeconomic characteristics and dispensed drug consumption. We defined a measure of proper ADHD diagnoses based on medical factors.We obtained an AUC of 79.6% with the stacked model. Significant variables that explain the ADHD presence are the dispersion across patients' visits to healthcare providers; the number of visits, diagnoses related to other mental disorders and drug consumption; age, and sex.ML techniques can help predict ADHD early diagnosis using administrative registers. We must continuously investigate the potential use of ADHD early detection strategies and intervention in the health system.

Authors & Co-authors:  Roche Mora Cid

Study Outcome 

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