Identifying non-adult attention-deficit/hyperactivity disorder individuals using a stacked machine learning algorithm using administrative data population registers in a universal healthcare system
Author
Roche, David
Mora, Toni
Cid, Jordi
Publication date
2023ISSN
2692-9384
Abstract
Background: 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. Methods: 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. Results: 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. Conclusions: 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.
Document Type
Article
Document version
Published version
Language
English
Subject (CDU)
61 - Medical sciences
Keywords
Trastorn per dèficit d'atenció amb hiperactivitat
Comorbilitat
Aprenentatge automàtic
Trastorno por déficit de atención con hiperactividad
Comorbilidad
Aprendizaje automático
Attention‐deficit hyperactivity disorder
Comorbidity
Machine learning
Pages
10
Publisher
John Wiley & Sons
Is part of
JCPP Advances
Citation
Roche, David; Mora, Toni; Cid, Jordi. Identifying non-adult attention-deficit/hyperactivity disorder individuals using a stacked machine learning algorithm using administrative data population registers in a universal healthcare system. JCPP Advances, 2023, e12193. Disponible en: <https://acamh.onlinelibrary.wiley.com/doi/full/10.1002/jcv2.12193>. Fecha de acceso: 17 oct. 2023. DOI: 10.1002/jcv2.12193
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Rights
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/