[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118974-en":3,"doc-seo-118974-105":30,"detail-sidebar-cat-0-en-105":96},{"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},118974,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors - a systematic review","Machine learning supports suicide prevention by integrating multiple risk factors and capturing complex interactions. This systematic review identifies studies applying machine learning to administrative and survey data, summarizes performance metrics, and enumerates key predictors of suicidal thoughts and behaviors. Searches across major biomedical databases (2019–2022) and eligible prior reviews were used, with predictive utility assessed through AUC distributions and summary statistics. Results show algorithm performance varies by suicide outcome, data source, and population.","TYPE Systematic Review PUBLISHED 04 March 2024  \nDOI 10.3389/fpsyt.2024.1291362  \nOPEN ACCESS  \nEDITED BY  \nAshwani Kumar Mishra,  \nAll India Institute of Medical Sciences, India  \nREVIEWED BY  \nRobert Suchting,  \nUniversity of Texas Health Science Center at Houston, United States  \nAnna Ceraso,  \nUniversity of Brescia, Italy  \n*CORRESPONDENCE Nibene H. Some´  \n [Nsome@uwo.ca](Nsome@uwo.ca)  \nRECEIVED 09 September 2023  \nACCEPTED 12 February 2024  \nPUBLISHED 04 March 2024  \nCITATION  \nSome´ NH, Noormohammadpour P and Lange S (2024) The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.  \nFront. Psychiatry 15:1291362 .  \ndoi: 10.3389/fpsyt.2024.1291362  \nCOPYRIGHT  \n© 2024 Some´ , Noormohammadpour and Lange. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nThe use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review  \nNibene H. Som´e1,2,3,4*, Pardis Noormohammadpour 1,4 and Shannon Lange 1,2,5  \n1 Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada, 2Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada, 3 Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada, 4 Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada, 5 Department of Psychiatry, University of Toronto, Toronto, ON, Canada  \nBackground: Machine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature.  \nObjective: Our study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified.  \nMethods: A systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughtsand behaviors using administrative and survey data was performed. The search was conducted for articles published between January 1, 2019 and May 11, 2022 . In addition, all articles identified in three recently published systematic reviews (the last of which included studies up until January 1, 2019) were retained if they met our inclusion criteria. The predictive power of machine learning methods in predicting suicidal thoughts and behaviors was explored using box plots to summarize the distribution of the area under the receiver operating characteristic curve (AUC) values by machine learning method and suicide outcome (i. e., suicidal thoughts, suicide attempt, and death by suicide) . Mean AUCs with 95% confidence intervals (CIs) were computed for each suicide outcome by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed.  \nResults: The search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. ","cbCaiihJa3COAySW","https://ap.wps.com/l/cbCaiihJa3COAySW","pdf",2175819,1,13,"English","en",105,"# Background\n# Objective\n# Methods\n# Results\n# Conclusion\n# Systematic review registration","[{\"question\":\"What is the main purpose of this systematic review?\",\"answer\":\"It identifies studies that use machine learning on administrative and survey data to predict suicidal thoughts and behaviors, summarizes reported performance metrics, and lists important risk factors.\"},{\"question\":\"Which types of data were included in the review?\",\"answer\":\"The review focuses on administrative data and survey data as the inputs for machine learning models predicting suicidal outcomes.\"},{\"question\":\"How did the review evaluate predictive performance?\",\"answer\":\"It used box plots and summary statistics of AUC values (including 95% confidence intervals) grouped by machine learning method and suicide outcome, such as suicidal thoughts, suicide attempt, and death by suicide.\"},{\"question\":\"What do the findings indicate about machine learning for suicide prediction?\",\"answer\":\"Predictive utility depends on the approach used, and risk factors differ by data source and the population studied, with performance varying across suicide outcomes.\"}]","The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors - 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