[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123976-en":3,"doc-seo-123976-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123976,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Prediction of adolescent depression from prenatal and childhood data from ALSPAC using machine learning","Depression contributes substantially to adolescent disability and mortality worldwide, with developmental pathways beginning years before diagnosis. A machine learning framework predicts adolescent depression at ages 12–18 using environmental, biological, and lifestyle features drawn from child, mother, and partner records across the prenatal period through age 10 from 8,467 ALSPAC participants. Cross-sectional and longitudinal models are trained and compared, achieving recall 0.59±0.20, specificity 0.61±0.17, and accuracy 0.64±0.13. The most informative predictors include female sex, parental depression and anxiety, and exposure to stressful events or environments, supporting early decision support for prevention.","UC Davis  \nUC Davis Previously Published Works  \nTitle  \nPrediction of adolescent depression from prenatal and childhood data from ALSPAC using machine learning  \nPermalink  \n[https://escholarship.org/uc/item/7xs6x39r](https://escholarship.org/uc/item/7xs6x39r)  \nJournal  \nScientific Reports, 14(1)  \nISSN  \n2045-2322  \nAuthors  \nYoo, Arielle  \nLi, Fangzhou Youn, Jason et al.  \nPublication Date  \n2024-10-01  \nDOI  \n10.1038/s41598-024-72158-9  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nPrediction of adolescent depression from prenatal and childhood data from ALSPAC using machine learning  \nArielle Yoo1,2,3,7, Fangzhou Li1,2,3,7, Jason Youn1,2,3,7, Joanna Guan4,5, Amanda E. Guyer 5,6, Camelia E. Hostinar4,5 & IliasTagkopoulos1,2,3*  \nDepression is a major cause of disability and mortality for young people worldwide and is typically first diagnosed during adolescence. In this work, we present a machine learning framework to predict adolescent depression occurring between ages 12 and 18 years using environmental, biological, and lifestyle features of the child, mother, and partner from the child’s prenatal period to age 10 years using data from 8467 participants enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC). We trained and compared several cross-sectional and longitudinal machine learning techniques and found the resulting models predicted adolescent depression with recall (0.59 ± 0.20), specificity (0.61 ± 0.17), and accuracy (0.64 ± 0.13), using on average 39 out of the 885 total features (4.4%) included in the models. The leading informative features in our predictive models of adolescent depression were female sex, parental depression and anxiety, and exposure to stressful events or environments. This work demonstrates how using a broad array of evidence-driven predictors from early in life can inform the development of preventative decision support tools to assist in the early detection of risk for mental illness.  \nDepression is an impairing and prevalent disorder, affecting approximately 34% of adolescents aged 10–19 years globally1. Depression is also one of the leading causes of non-fatal disability2 and a major risk factor for suicide, the second leading cause of death for people aged 10–34 in the United States3. The main symptoms of depression include low mood, diminished interest or pleasure, change in sleep, weight or appetite, decrease in energy, feelings of worthlessness or guilt, and frequent thoughts of death or suicide4. Although these symptoms most often reach clinical levels of concern leading to diagnosis during adolescence, the developmental processes resulting in adolescent depression start years prior5, potentially even during gestation6. Through a hypothesized mechanism of stress sensitization, exposure to adverse early-life experiences such as parental mental illness or financial hardship is thought to increase the risk of future depression7,8. The connection between challenging childhood experiences and the risk of depression offers a chance to identify at-risk children early. Identification can be done by combining information about stressful events children have faced with data from comprehensive developmental assessments linked to depression risk (e.g., socio-emotional, cognitive, and biological) . Early identification by age 10 of children at risk of developing depression during their adolescent years would provide new avenues for preemptive interventions, thereby reducing suffering, adolescent mortality, and treatment costs associated with depression9, 10.  \nOne prominent depression model is the multilevel biopsychosocial model put forth by Garber5. This model is based on evidence that depression is a complex condition influenced by various social-contextual, psychological, and biological factors, with no single fa","cbCaiqNbCaicDqku","https://ap.wps.com/l/cbCaiqNbCaicDqku","pdf",1613922,1,14,"English","en",105,"# Abstract\n## Clinical background and rationale\n## Data source and modeling approach\n## Performance results and key features\n## Implications for early prevention","[{\"question\":\"What ages does the model aim to predict for adolescent depression?\",\"answer\":\"The framework predicts adolescent depression occurring between ages 12 and 18 years.\"},{\"question\":\"What data are used to build the prediction features?\",\"answer\":\"Features come from the child, mother, and partner across the prenatal period through age 10, drawn from the ALSPAC cohort.\"},{\"question\":\"Which factors are highlighted as most informative for the predictive models?\",\"answer\":\"Female sex, parental depression and anxiety, and exposure to stressful events or environments are identified as leading informative features.\"}]","Prediction of adolescent depression from prenatal and childhood data from ALSPAC using machine learning | 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ages does the model aim to predict for adolescent depression?","Question",{"text":75,"@type":76},"The framework predicts adolescent depression occurring between ages 12 and 18 years.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data are used to build the prediction features?",{"text":80,"@type":76},"Features come from the child, mother, and partner across the prenatal period through age 10, drawn from the ALSPAC cohort.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors are highlighted as most informative for the predictive models?",{"text":84,"@type":76},"Female sex, parental depression and anxiety, and exposure to stressful events or environments are identified as leading informative 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