{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T18:42:55Z","timestamp":1785436975375,"version":"3.56.0"},"reference-count":44,"publisher":"Royal College of Psychiatrists","issue":"1","license":[{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"unspecified","delay-in-days":46,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Eur. Psychiatr."],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"S0924933826101710_sec1\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Attention deficit hyperactivity disorder (ADHD) affects 5\u20137.2% of children and 2.5% of adults. Despite its prevalence, ADHD remains underdiagnosed and undertreated, leading to significant challenges for affected individuals. Early diagnosis and intervention can prevent adverse outcomes and improve quality of life.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0924933826101710_sec2\" sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a predictive model to identify adults with ADHD using electronic health records. The dataset comprised 2,973 adult patients (aged 18 years and above) diagnosed with ADHD and a control group of 4,447 adults referred to psychologists with no ADHD diagnosis. A transformer-based architecture was implemented, utilizing only clinical codes and gender as input features. Fivefold cross-validation was adopted, and model performance was evaluated on held-out test data consisting of 800 patients, 400 of whom had an ADHD diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0924933826101710_sec3\" sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our study demonstrated the ability to predict adult ADHD using clinical data, with a 6-month model achieving an area under the receiver operating characteristic curve (AUC) of 0.79 (95% confidence interval: 0.76\u20130.81), F1-score of 0.79, sensitivity of 0.80, and specificity of 0.77. Shapley Additive Explanations identified key contributing codes, including F158 and Y903, consistent with known associations between ADHD and substance use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0924933826101710_sec4\" sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our findings show that machine learning can effectively use clinical codes and demographic data from routine EHRs to support early, cost-efficient diagnosis of adult ADHD, paving the way for earlier intervention and improved outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1192\/j.eurpsy.2026.10171","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T09:02:21Z","timestamp":1771232541000},"source":"Crossref","is-referenced-by-count":1,"title":["Early detection of adults ADHD using electronic health records: A machine learning 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