[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117361-en":3,"doc-seo-117361-105":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},117361,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Leveraging Machine Learning to Predict Adolescent Suicidality","The thesis investigates how machine learning can be used to predict adolescent suicidality, aiming to contribute evidence to suicidology and improve vulnerability identification. It outlines how suicidality may arise and examines pathways through which adolescents can become vulnerable to suicide. The work emphasizes a careful, non-graphic presentation, while also encouraging readers who experience difficult emotions to seek support. Resources for crisis help are provided for Australia and internationally.","Leveraging Machine Learning to Predict Adolescent Suicidality  \nAuthor:  \nKusuma , Karen  \nPublication Date:  \n2024  \nDOI:  \n[https://doi.org/10.26190/unsworks/30747](https://doi.org/10.26190/unsworks/30747)  \nLicense:  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nLink to license to see what you are allowed to do with this resource.  \nDownloaded from [http://hdl.handle. net/1959.4/103740](http://hdl.handle. net/1959.4/103740) in [https://](https://)[ ](https://)[unsworks. unsw.edu.au](unsworks. unsw.edu.au) on 2025-04-18  \nLeveraging Machine Learning to Predict Adolescent Suicidality  \nKaren Kusuma  \nA thesis in fulfilment of the requirements for the degree of Doctor of Philosophy  \nSchool of Clinical Medicine  \nFaculty of Medicine and Health  \n2024  \nAcknowledgements  \nAs much as this felt at times to be a deeply personal journey, the contributions of everyone else must not be understated. I formally thank everyone who has helped shaped this thesis over these long years.  \nIt has been a great privilege to be supervised by a stellar multi-disciplinary team of renown researchers. A/Prof Mark Larsen-thank you for kindness throughout this whole process, for your reliable provision of level-headed guidance. A/Prof Michelle Tye-thank you for believing in me by granting me this life-changing opportunity, and for steering me in the right direction. Dr Juan Quiroz Aguilera-thank you for guiding me as I ventured into machine learning, and helping me procure a desk at CBDRH. I wouldn’t have made it here without any of you, and I have learned so much under your supervision.  \nI sincerely thank the Anika Foundation, particularly Dr Adrian Blundell-Wignall, for supporting this work. It is a great honour to be a small part ofAnika’s beautiful legacy.  \nOne of the best parts of doing a cross-disciplinary PhD is getting to meet many different types of brilliant people. I’d like to acknowledge all the kind colleagues I met at Black Dog Institute. Having learned from BDI’s numerous mental health resources as an adolescent, I feel like I am coming full circle by finishing this PhD. Special thanks to the EMERGE team, for all the opportunities and ramen catchups I had a pleasure to attend. Thank you to my colleagues at the Center for Big Data Research in Health, who warmly welcomed me into their office. I especially enjoyed the Machine Learning Club sessions and learning together with everyone else. In these workplaces, I had the pleasure to meet other fellow PhD students: Clara Khuon, Dr Echo Xu, Dr Daniel Gan, Anupama Thabrew, and Larry Bokang Bi (in no particular order!). I am so happy to have met each of you. Your intelligence has inspired me, and I am excited to watch you go onto even greater things.  \nThis PhD would also be impossible without all the loving and unconditional social support I enjoyed. To my parents, thank you for coming to Sydney in the final month to offer in-person support. Dr Conor Gribbin-thank you for the candid recaps of your clinical work in child and adolescent psychiatry. They grounded this thesis, and continuously reminded me of how we need to keep working on the evidence base. Thank you to my old friends in Brisbane, and my new friends in Sydney. Now you no longer have to ask me how my thesis is going! Special thanks to Lynette Tan, my oldest and dearest friend, for being possibly the most excited person in this submission process.  \nA note on content  \nDear reader,  \nAs I undertook this thesis in a bid to understand and contribute a little to the field of suicidology, the following chapters will discuss how suicidality may arise, and the various ways one might be vulnerable to suicide. There will be no graphic descriptions of violence or suicide, nor will it delve into the lived experience of suicide. Nevertheless, if this thesis at any point elicits difficult emotions, I urge you to seek help from a loved one, or if available, a mental health professional.  \nOthe","cbCaiv0R40bBIH9e","https://ap.wps.com/l/cbCaiv0R40bBIH9e","pdf",5502065,1,244,"English","en",105,"# Acknowledgements\n# Figures\n# Tables\n# Abbreviations\n# Abstract\n# Thesis Overview\n# Chapter 1. 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