{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T02:00:31Z","timestamp":1789783231749,"version":"4.0.1"},"reference-count":43,"publisher":"American Medical Association (AMA)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Psychiatry"],"published-print":{"date-parts":[[2023,2,1]]},"abstract":"\n Importance<\/jats:title>\n Diagnoses and treatment of mental disorders are hampered by the current lack of objective markers needed to provide a more precise diagnosis and treatment strategy.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To develop deep learning models to predict mental disorder diagnosis and severity spanning multiple diagnoses using nationwide register data, family and patient-specific diagnostic history, birth-related measurement, and genetics.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n This study was conducted from May 1, 1981, to December 31, 2016. For the analysis, which used a Danish population-based case-cohort sample of individuals born between 1981 and 2005, genotype data and matched longitudinal health register data were taken from the longitudinal Danish population-based Integrative Psychiatric Research Consortium 2012 case-cohort study. Included were individuals with mental disorders (attention-deficit\/hyperactivity disorder [ADHD]), autism spectrum disorder (ASD),\u2009major depressive disorder (MDD), bipolar disorder (BD), schizophrenia spectrum disorders (SCZ), and population controls. Data were analyzed from February 1, 2021, to January 24, 2022.<\/jats:p>\n <\/jats:sec>\n \n Exposure<\/jats:title>\n At least 1 hospital contact with diagnosis of ADHD, ASD, MDD, BD, or SCZ.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n The predictability of (1) mental disorder diagnosis and (2) severity trajectories (measured by future outpatient hospital contacts, admissions, and suicide attempts) were investigated using both a cross-diagnostic and single-disorder setup. Predictive power was measured by AUC, accuracy, and Matthews correlation coefficient (MCC), including an estimate of feature importance.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n A total of 63\u202f535 individuals (mean [SD] age, 23 [7] years; 34\u202f944 male [55%]; 28\u202f591 female [45%]) were included in the model. Based on data prior to diagnosis, the specific diagnosis was predicted in a multidiagnostic prediction model including the background population with an overall area under the curve (AUC) of 0.81 and MCC of 0.28, whereas the single-disorder models gave AUCs\/MCCs of 0.84\/0.54 for SCZ, 0.79\/0.41 for BD, 0.77\/0.39 for ASD, 0.74\/0.38, for ADHD, and 0.74\/0.38 for MDD. The most important data sets for multidiagnostic prediction were previous mental disorders and age (11%-23% reduction in prediction accuracy when removed) followed by family diagnoses, birth-related measurements, and genetic data (3%-5% reduction in prediction accuracy when removed). Furthermore, when predicting subsequent disease trajectories of the disorder, the most severe cases were the most easily predictable, with an AUC of 0.72.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n Results of this diagnostic study suggest the possibility of combining genetics and registry data to predict both mental disorder diagnosis and disorder progression in a clinically relevant, cross-diagnostic setting prior to clinical assessment.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamapsychiatry.2022.4076","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T11:01:31Z","timestamp":1670410891000},"page":"146","source":"Crossref","is-referenced-by-count":45,"title":["Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data"],"prefix":"10.1001","volume":"80","author":[{"given":"Rosa 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