{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:16:40Z","timestamp":1750220200533,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T00:00:00Z","timestamp":1652400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme (FRGS),","award":["FRGS\/1\/2020\/ICT02\/USM\/02\/5"],"award-info":[{"award-number":["FRGS\/1\/2020\/ICT02\/USM\/02\/5"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"DOI":"10.1145\/3545729.3545754","type":"proceedings-article","created":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T16:13:55Z","timestamp":1665677635000},"page":"118-123","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Machine Learning Models for Suicidal Behavior Prediction"],"prefix":"10.1145","author":[{"given":"NORATIKAH","family":"NORDIN","sequence":"first","affiliation":[{"name":"School of Computer Sciences, Universiti Sains Malaysia, Malaysia"}]},{"given":"ZURINAHNI","family":"ZAINOL","sequence":"additional","affiliation":[{"name":"School of Computer Sciences, Universiti Sains Malaysia, Malaysia"}]},{"given":"MOHD HALIM","family":"MOHD NOOR","sequence":"additional","affiliation":[{"name":"School of Computer Sciences, Universiti Sains Malaysia, Malaysia"}]},{"given":"CHAN LAI","family":"FONG","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Universiti Kebangsaan Malaysia Medical Centre, Malaysia"}]}],"member":"320","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"World Health Organization. Suicide. Retrieved Aug 20 2021 from https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/suicide  World Health Organization. Suicide. Retrieved Aug 20 2021 from https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/suicide"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S2215-0366(14)70265-2","volume":"1","author":"O'Connor Rory C","year":"2014","unstructured":"Rory C O'Connor and Matthew K Nock . The psychology of suicidal behaviour. Lancet Psychiatry. 1 , 1 ( Jun. 2014 ), 73\u201385. doi: 10.1016\/S2215-0366(14)70222-6. Rory C O'Connor and Matthew K Nock. The psychology of suicidal behaviour. Lancet Psychiatry. 1, 1 (Jun. 2014), 73\u201385. doi: 10.1016\/S2215-0366(14)70222-6.","journal-title":"Lancet Psychiatry."},{"key":"e_1_3_2_1_3_1","first-page":"6","volume":"14","author":"Jung Jun Su","year":"2019","unstructured":"Jun Su Jung , Sung Jin Park , Eun Young Kim , Kyoung-Sae Na , Young Jae Kim , and Kwang Gi Kim . Prediction models for high risk of suicide in Korean adolescents using machine learning techniques. PLOS ONE. 14 , 6 ( June 2019 ), e0217639. doi: 10.1371\/journal.pone.0217639. Jun Su Jung, Sung Jin Park, Eun Young Kim, Kyoung-Sae Na, Young Jae Kim, and Kwang Gi Kim. Prediction models for high risk of suicide in Korean adolescents using machine learning techniques. PLOS ONE. 14, 6 (June 2019), e0217639. doi: 10.1371\/journal.pone.0217639.","journal-title":"PLOS ONE."},{"key":"e_1_3_2_1_4_1","first-page":"4","volume":"17","author":"Oh Bumjo","year":"2020","unstructured":"Bumjo Oh , Je-Yeon Yun , Eun Chong Yeo , Dong-Hoi Kim , Jin Kim , and Bum-Joo Cho . Prediction of suicidal ideation among korean adults using machine learning: A cross-sectional study. Psychiatry Investig. 17 , 4 ( April 2020 ), 331\u2013340. doi: 10.30773\/pi.2019.0270. Bumjo Oh, Je-Yeon Yun, Eun Chong Yeo, Dong-Hoi Kim, Jin Kim, and Bum-Joo Cho. Prediction of suicidal ideation among korean adults using machine learning: A cross-sectional study. Psychiatry Investig. 17, 4 (April 2020), 331\u2013340. doi: 10.30773\/pi.2019.0270.","journal-title":"Psychiatry Investig."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2018.11.073"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1037\/bul0000084","volume":"143","author":"Franklin C.","year":"2017","unstructured":"Joseph. C. Franklin , Jessica D. Ribeiro Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychol. Bull. 143 , 2 ( January 2017 ), 187\u2013232. doi: 10.1037\/bul0000084. Joseph. C. Franklin, Jessica D. Ribeiro Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychol. Bull. 143, 2 (January 2017), 187\u2013232. doi: 10.1037\/bul0000084.","journal-title":"Psychol. Bull."},{"key":"e_1_3_2_1_7_1","first-page":"12","volume":"13","author":"Abdullah Talal A. A.","year":"2021","unstructured":"Talal A. A. Abdullah , Mohd Soperi Mohd Zahid , and Waleed Ali . A review of interpretable ml in healthcare: taxonomy, applications, challenges, and future directions. Symmetry. 13 , 12 ( December 2021 ), 2439. doi: 10.3390\/sym13122439. Talal A. A. Abdullah, Mohd Soperi Mohd Zahid, and Waleed Ali. A review of interpretable ml in healthcare: taxonomy, applications, challenges, and future directions. Symmetry. 13, 12 (December 2021), 2439. doi: 10.3390\/sym13122439.","journal-title":"Symmetry."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3389\/fpsyt.2019.00036","volume":"10","author":"Velupillai Sumitra","year":"2019","unstructured":"Sumitra Velupillai , Gerfo Hadlaczky Risk assessment tools and data-driven approaches for predicting and preventing suicidal behavior. Front. Psychiatry. 10 , 36 ( February 2019 ), 1-8. doi: 10.3389\/fpsyt.2019.00036. Sumitra Velupillai, Gerfo Hadlaczky Risk assessment tools and data-driven approaches for predicting and preventing suicidal behavior. Front. Psychiatry. 10, 36 (February 2019), 1-8. doi: 10.3389\/fpsyt.2019.00036.","journal-title":"Front. Psychiatry."},{"key":"e_1_3_2_1_9_1","first-page":"9","volume":"45","author":"Amini Payan","year":"2016","unstructured":"Payan Amini , Hasan Ahmadinia , Jalal Poorolajal , and Mohammad Moqaddasi Amiri . Evaluating the high-risk groups for suicide: A comparison of logistic regression, support vector machine, decision tree and artificial neural network. Iran. J. Public Health. 45 , 9 ( September 2016 ), 1179\u20131187. https:\/\/ijph.tums.ac.ir\/index.php\/ijph. Payan Amini, Hasan Ahmadinia, Jalal Poorolajal, and Mohammad Moqaddasi Amiri. Evaluating the high-risk groups for suicide: A comparison of logistic regression, support vector machine, decision tree and artificial neural network. Iran. J. Public Health. 45, 9 (September 2016), 1179\u20131187. https:\/\/ijph.tums.ac.ir\/index.php\/ijph.","journal-title":"Iran. J. Public Health."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.jpsychires.2020.10.024","volume":"136","author":"Edgcomb Juliet Beni","year":"2021","unstructured":"Juliet Beni Edgcomb , Trevor Shaddox , Gerhard Hellemann , and John O. Brooks . Predicting suicidal behavior and self-harm after general hospitalization of adults with serious mental illness. J. Psychiatr. Res. , 136 ( April 2021 ), 515 \u2013 521 . doi: 10.1016\/j.jpsychires.2020.10.024. Juliet Beni Edgcomb, Trevor Shaddox, Gerhard Hellemann, and John O. Brooks. Predicting suicidal behavior and self-harm after general hospitalization of adults with serious mental illness. J. Psychiatr. Res., 136 (April 2021), 515\u2013521. doi: 10.1016\/j.jpsychires.2020.10.024.","journal-title":"J. Psychiatr. Res."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-020-0780-3"},{"key":"e_1_3_2_1_12_1","first-page":"3","volume":"51","author":"Horvath Adam","year":"2021","unstructured":"Adam Horvath , Mark Dras , Catie C. W. Lai , and Simon Boag . Predicting suicidal behavior without asking about suicidal ideation: machine learning and the role of borderline personality disorder criteria. Suicide Life. Threat. Behav. 51 , 3 ( June 2021 ), 455\u2013466. doi: 10.1111\/sltb.12719. Adam Horvath, Mark Dras, Catie C. W. Lai, and Simon Boag. Predicting suicidal behavior without asking about suicidal ideation: machine learning and the role of borderline personality disorder criteria. Suicide Life. Threat. Behav. 51, 3 (June 2021), 455\u2013466. doi: 10.1111\/sltb.12719.","journal-title":"Suicide Life. Threat. Behav."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","first-page":"688969","DOI":"10.3389\/fdata.2021.688969","volume":"4","author":"Belle Vaishak","year":"2021","unstructured":"Vaishak Belle and Ioannis Papantonis . Principles and practice of explainable machine learning. Front. Big Data. 4 ( July 2021 ), 688969 . doi: 10.3389\/fdata.2021.688969. Vaishak Belle and Ioannis Papantonis. Principles and practice of explainable machine learning. Front. Big Data. 4 (July 2021), 688969. doi: 10.3389\/fdata.2021.688969.","journal-title":"Front. Big Data."},{"key":"e_1_3_2_1_14_1","first-page":"2","volume":"11","author":"Leo Diego De","year":"2021","unstructured":"Diego De Leo , Benjamin Goodfellow International study of definitions of English-language terms for suicidal behaviours: A survey exploring preferred terminology. BMJ Open. 11 , 2 ( February 2021 ), e043409. doi: 10.1136\/bmjopen-2020-043409. Diego De Leo, Benjamin Goodfellow International study of definitions of English-language terms for suicidal behaviours: A survey exploring preferred terminology. BMJ Open. 11, 2 (February 2021), e043409. doi: 10.1136\/bmjopen-2020-043409.","journal-title":"BMJ Open."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1027\/0227-5910\/a000046","volume":"32","author":"Chan Lai Fong","year":"2011","unstructured":"Lai Fong Chan , T. Maniam , and A. S. Shamsul . Suicide attempts among depressed inpatients with depressive disorder in a Malaysian sample: Psychosocial and clinical risk factors. Crisis. 32 , 5 ( September 2011 ), 283\u2013287. doi: 10.1027\/0227-5910\/a000088. Lai Fong Chan, T. Maniam, and A. S. Shamsul. Suicide attempts among depressed inpatients with depressive disorder in a Malaysian sample: Psychosocial and clinical risk factors. Crisis. 32, 5 (September 2011), 283\u2013287. doi: 10.1027\/0227-5910\/a000088.","journal-title":"Crisis."},{"key":"e_1_3_2_1_16_1","first-page":"12","volume":"15","author":"Iorfino Frank","year":"2020","unstructured":"Frank Iorfino , Nicholas Ho Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study. PLOS ONE. 15 , 12 , ( December 2020 ), e0243467. doi: 10.1371\/journal.pone.0243467. Frank Iorfino, Nicholas Ho Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study. PLOS ONE. 15, 12, (December 2020), e0243467. doi: 10.1371\/journal.pone.0243467.","journal-title":"PLOS ONE."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1590\/1516-4446-2015-1877","volume":"39","author":"Barros Jorge","year":"2016","unstructured":"Jorge Barros , Susana Morales Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders. Rev. Bras. Psiquiatr. 39 , 1 ( October 2016 ), 1\u201311. doi: 10.1590\/1516-4446-2015-1877. Jorge Barros, Susana Morales Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders. Rev. Bras. Psiquiatr. 39, 1 (October 2016), 1\u201311. doi: 10.1590\/1516-4446-2015-1877.","journal-title":"Rev. Bras. Psiquiatr."},{"key":"e_1_3_2_1_18_1","volume-title":"Proceedings of 2016 ICML Workshop on Human Interpretability in Machine Learning","author":"Ribeiro Marco Tulio","year":"2016","unstructured":"Marco Tulio Ribeiro , Sameer Singh , and Carlos Guestrin . 2016 . Model-agnostic interpretability of machine learning . In Proceedings of 2016 ICML Workshop on Human Interpretability in Machine Learning , New York, USA, 91 -95. doi: 10.48550\/ARXIV.1606.05386. Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. Model-agnostic interpretability of machine learning. In Proceedings of 2016 ICML Workshop on Human Interpretability in Machine Learning, New York, USA, 91 -95. doi: 10.48550\/ARXIV.1606.05386."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295230"},{"key":"e_1_3_2_1_20_1","first-page":"1","volume":"27","author":"Nordin Noratikah","year":"2021","unstructured":"Noratikah Nordin , Zurinahni Zainol , Mohd Halim Mohd Noor , and C. Lai Fong . A comparative study of machine learning techniques for suicide attempts predictive model. Health Informatics J. 27 , 1 ( January 2021 ), 146045822198939. doi: 10.1177\/1460458221989395. Noratikah Nordin, Zurinahni Zainol, Mohd Halim Mohd Noor, and C. Lai Fong. A comparative study of machine learning techniques for suicide attempts predictive model. Health Informatics J. 27, 1 (January 2021), 146045822198939. doi: 10.1177\/1460458221989395.","journal-title":"Health Informatics J."},{"volume-title":"Applied predictive modeling","author":"Kuhn Max","key":"e_1_3_2_1_21_1","unstructured":"Max Kuhn and Kjell Johnson . 2013. Applied predictive modeling . Springer . New York, USA. doi:10.1007\/978-1-4614-6849-3. Max Kuhn and Kjell Johnson. 2013. Applied predictive modeling. Springer. New York, USA. doi:10.1007\/978-1-4614-6849-3."}],"event":{"name":"ICMHI 2022: 2022 6th International Conference on Medical and Health Informatics","acronym":"ICMHI 2022","location":"Virtual Event Japan"},"container-title":["2022 6th International Conference on Medical and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3545729.3545754","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3545729.3545754","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:46Z","timestamp":1750186966000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3545729.3545754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,13]]},"references-count":21,"alternative-id":["10.1145\/3545729.3545754","10.1145\/3545729"],"URL":"https:\/\/doi.org\/10.1145\/3545729.3545754","relation":{},"subject":[],"published":{"date-parts":[[2022,5,13]]}}}