[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118208-en":3,"doc-seo-118208-105":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":4,"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},118208,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","An Umbrella Review for Machine Learning Suicide Prediction and Prevention in Mental Health","This research presents an umbrella review centered on how machine learning supports the prediction and prevention of suicide in mental health. It synthesizes recent studies to surface research gaps and to evaluate strengths, limitations, and ethical considerations in using machine learning to detect suicidal thoughts. Results suggest potential gains in intervention efficacy and prediction accuracy, while emphasizing the need for rigorous validation, ethical transparency, and clinical expertise integration. The review also highlights future work on personalized interventions, improved data interoperability, and expanded data sources.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| AMCIS 2024 Proceedings | Americas Conference on Information Systems\u003Cbr>(AMCIS) |\n| --- | --- |\n| August 2024\u003Cbr>An Umbrella Review for Machine Learning Suicide Prediction and Prevention in Mental Health\u003Cbr>Kaidi Huang\u003Cbr>Worcester Polytechnic Institute, [khuang6@wpi.edu](khuang6@wpi.edu)\u003Cbr>Bengisu Tulu\u003Cbr>WPI, [bengisu@wpi.edu](bengisu@wpi.edu)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/amcis2024](https://aisel.aisnet.org/amcis2024) |  |\n\nRecommended Citation  \nHuang, Kaidi and Tulu, Bengisu, \"An Umbrella Review for Machine Learning Suicide Prediction and Prevention in Mental Health\" (2024) . AMCIS 2024 Proceedings. 17.  \n[https://aisel.aisnet.org/amcis2024/health_it/health_it/17](https://aisel.aisnet.org/amcis2024/health_it/health_it/17)  \nThis material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in AMCIS 2024 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact elibrary@aisnet.org](contact elibrary@aisnet.org).  \nMachine Learning for Suicide Prevention: An Umbrella Review  \nAn Umbrella Review for Machine Learning Suicide Prediction and Prevention in Mental  \nHealth  \nCompleted Research Full Paper  \nKaidi Huang  \nWorcester Polytechnic Institute [khuang6@wpi.edu](khuang6@wpi.edu)  \nBengisu Tulu  \nWorcester Polytechnic Institute [bengisu@wpi.edu](bengisu@wpi.edu)  \nAbstract  \nThis research presents an umbrella review that mainly focuses on the application of machine learning in the prediction and prevention of suicide, identifying research gaps and proposing future research directions for this field. By accessing recent studies, this review identifies the strengths, limitations, and ethical considerations of utilizing machine learning to predict suicidal thoughts. Key findings indicate that machine learning has the potential to increase intervention efficacy and prediction accuracy, but they also need for rigorous validation, ethical transparency, and integration of clinical expertise. In addition, thereview promotes a multidisciplinary approach to integrate machine learning techniques with real-world clinical applications. Future directions are suggested to focus on personalized intervention strategies, improving data interoperability, and exploring novel data sources to refine and expand the use of machine learning in this area of mental health.  \nKeywords  \nMachine Learning, Suicide Prevention, Predictive Analytics in Healthcare, Algorithms, Predictive Models.  \nIntroduction  \nRising number of suicides, reflecting the struggles and helplessness prevalent in modern society, is a worldwide concern (World Health Organization, n.d.) . The causes of suicidal behavior are complex and varied, including mental health problems, socioeconomic factors, cultural background, and personal experiences. The global suicide rates are between 10.5 and 13.5 per 100,000 people, with 13.5 for men and 7.7 for women (Edina and Attila 2021) . These figures emphasize the importance and urgency of suicide prevention. The development of effective suicide prevention strategies is not only critical to saving lives, but also to maintaining social stability and well-being.  \nSuicide prevention plays an important role in public health. Effective suicide prevention strategies save countless lives and reduce the psychological trauma and social costs associated with suicide (Javadi et al. 2022) . From early identification of people at risk for suicide to timely mental health support and intervention, each step is important to preventing suicidal behaviors. In addition, the development and implementation of suicide prevention strategies must consider cultural, socioeconomic, and individual differences to ensure their effectiveness and sustainability. While traditional suicide prevention approaches are","cbCaivSsOobmtALl","https://ap.wps.com/l/cbCaivSsOobmtALl","pdf",380280,1,11,"English","en",105,"# Introduction\n## Suicide prevention and public health urgency\n## Limits of traditional risk assessment tools\n## Machine learning as an emerging approach\n# Machine Learning for Suicide Prevention: An Umbrella Review\n## Role of machine learning in decision support\n## Data sources and model applications","[{\"question\":\"What is the main purpose of the umbrella review?\",\"answer\":\"To focus on applying machine learning to suicide prediction and prevention in mental health, identify research gaps, and propose future research directions.\"},{\"question\":\"What strengths and limitations are highlighted for machine learning in suicide prediction?\",\"answer\":\"The review indicates that machine learning may improve intervention efficacy and prediction accuracy, but it also requires rigorous validation and ethical transparency while addressing limitations in model reliability.\"},{\"question\":\"Which future research directions does the review recommend?\",\"answer\":\"It recommends personalized intervention strategies, improving data interoperability, and exploring novel data sources to refine and expand machine learning use in this area of mental health.\"}]","An Umbrella Review for Machine Learning Suicide Prediction and Prevention in Mental Health | PDF",1785682210,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-umbrella-review-for-machine-learning-suicide-prediction-and-prevention-in-mental-health","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-umbrella-review-for-machine-learning-suicide-prediction-and-prevention-in-mental-health/118208/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main purpose of the umbrella review?","Question",{"text":75,"@type":76},"To focus on applying machine learning to suicide prediction and prevention in mental health, identify research gaps, and propose future research directions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What strengths and limitations are highlighted for machine learning in suicide prediction?",{"text":80,"@type":76},"The review indicates that machine learning may improve intervention efficacy and prediction accuracy, but it also requires rigorous validation and ethical transparency while addressing limitations in model reliability.",{"name":82,"@type":73,"acceptedAnswer":83},"Which future research directions does the review recommend?",{"text":84,"@type":76},"It recommends personalized intervention strategies, improving data interoperability, and exploring novel data sources to refine and expand machine learning use in this area of mental health.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]