{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Roche D"],"funding":["Ministry of Science and Innovation"],"pagination":["e12193"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10933630"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["4(1)"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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."],"journal":["JCPP advances"],"pubmed_title":["Identifying non-adult attention-deficit/hyperactivity disorder individuals using a stacked machine learning algorithm using administrative data population registers in a universal healthcare system."],"pmcid":["PMC10933630"],"funding_grant_id":["PID2021‐124067OB‐C21"],"pubmed_authors":["Mora T","Cid J","Roche D"],"additional_accession":[]},"is_claimable":false,"name":"Identifying non-adult attention-deficit/hyperactivity disorder individuals using a stacked machine learning algorithm using administrative data population registers in a universal healthcare system.","description":"<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2026-07-08T03:14:21.486Z","creation":"2025-02-19T03:09:59.425Z"},"accession":"S-EPMC10933630","cross_references":{"pubmed":["38486959"],"doi":["10.1002/jcv2.12193"]}}