Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning

Cited by (264)

Items 201–264 of 264

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6. Computational Methods to Predict Suicide Ideation among Adolescents
   Sherin Rappai, Gobi Ramasamy · 2022 · International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)
7. Suicide Ideation Detection: Harnessing Machine and Deep Learning for Early Risk Identification
   Himanshu Gupta, Kamal Kumar Gola, Sumit Kumar, Mridula, Prashant Kumar, Narayan Jee · 2023 · International Conference on Contemporary Computing and Informatics (IC3I)
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9. Foundational skills in the assessment and management of suicide risk in neuropsychological practice
   Matthew R. Calamia, Raymond P. Tucker, Ryan M. Hill · 2024 · The Clinical Neuropsychologist
10. Precision medicine in the assessment of suicide risk
    Thiago Henrique Roza, Lucas Mohr Patusco, Aline Zimerman, Pedro L. Ballester, Ives Cavalcante Passos · 2019 · Elsevier eBooks
11. Implementing Predictive Models Within an Electronic Health Record System: Lessons from an External Validation of a Suicide Risk Model
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12. Predicting suicidal thoughts and behaviors among college students: a machine learning approach
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14. Interpretations of Innovation: The intersection of technological advancement and psychosis (Preprint)
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15. Advances in Research on Adolescent Suicide and a High Priority Agenda for Future Research
    Matthew Graham Clayton, Olivia Havemeyer Pollak, Sarah Owens, Adam Bryant Miller, Mitchell J. Prinstein · 2021
16. Randomized Controlled Comparative Effectiveness Trial of Risk Model-Guided Clinical Decision Support for Suicide Screening
    Colin G. Walsh, Michael A. Ripperger, Laurie Lovett Novak, Carrie Reale, Shilo H. Anders, Ashley Spann, Jhansi Kolli, Katelyn Robinson, Qingxia Chen, David Alan Isaacs et al. · 2024 · medRxiv
17. Machine Learning Approaches for Psychological Research Review
    Marta R. Jabłońska, Zdzisław Pólkowski · 2021 · Lecture notes on data engineering and communications technologies
18. A digital companion for ecological momentary assessment and prevention of suicide: A case series on the use of the emma app
    Margot Morgiève, catherine Genty, Jonathan Dubois, Marion Leboyer, Guillaume Vaïva, Sofian Berrouiguet, Philippe Courtet · 2020
19. Predicting Suicide Risk in Turkey Using Machine Learning
    Elif Şanlıalp, İbrahim Şanlıalp, Tuncay Yi̇ği̇t · 2021 · Lecture notes on data engineering and communications technologies
20. PREDICTING SELF-HARM AND IDENTIFYING CAUSAL RISK FACTORS AMONG ADOLESCENTS WHO HAVE HAD CONTACT WITH U.S. CHILD PROTECTIVE SERVICES
    Geoffrey D. Kahn · 2021
21. Using Implicit and Explicit Measures to Predict Suicidal Behavior Among Adolescents
    Katherine M. Tezanos, Kerri‐Anne Bell, Christine B. Cha · 2021 · Springer eBooks
22. Depression, Suicidal Tendencies, Hopelessness, and Stress among Patients with Learning Disabilities
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23. Supplemental Material for Mind-Reading Machines: Distinct User Responses to Thought-Detecting and Emotion-Detecting Robots
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24. Detection of Suicidal Behavior and Self-harm Among Children Presenting to Emergency Departments: A Tree-based Classification Approach.
    Juliet Beni Edgcomb, Chi‐Hong Tseng, Mengtong Pan, Alexandra M. Klomhaus, Bonnie T. Zima · 2023 · PubMed Central
25. Innovation of Digital Health Technologies for Screening and Mitigation of the Mental Health Consequences of Adverse Childhood Experiences: A Scoping Review
    Brianna M White, Rameshwari Prasad, Nariman Ammar, Jason A. Yaun, Arash Shaban‐Nejad · 2023 · Research Square
26. Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention
    Brian D. Williamson, Erica E. M. Moodie, Gregory E. Simon, Rebecca C. Rossom, Susan M. Shortreed · 2026 · The Annals of Applied Statistics
27. Exploring adolescent healthcare provider perspectives on machine learning suicide risk classification using a mixed-methods approach (Preprint)
    Pua Lani Yang, Laura Nicole Sisson, Mira A. Bajaj, Emily E. Haroz · 2024
28. An Efficient Predictive Model for Suicide Attempts in Bangladesh Using Machine Learning Algorithms
    Sumaiya Khan Ena, Alomgir Hossain, Nur Anisah Mohamed A. Rahman · 2024 · Algorithms for intelligent systems
29. Suizidalität im Kindes- und Jugendalter
    Paul Lukas Plener, Michael Kaess · 2024 · Springer Reference Medizin
30. Identifying Adolescent Depression and Anxiety through Real-world Data and Social Determinants of Health (Preprint)
    Mamoun Mardini, Georges Elias Khalil, Chen Bai, Aparna Menon DivaKaran, Jessica M. Ray · 2024
31. A REVIEW ON MACHINE LEARNING APPROACHES FOR THE DETECTION OF SUICIDAL TENDENCIES
    Kazi Golam Rabbany, Aisultan Shoiynbek, Darkhan Kuanyshbay, Assylbek Mukhametzhanov, Akbayan Bekarystankyzy, Temirlan Shoiynbek · 2024 · World Science
32. Artificial Intelligence as a Tool to Prevent Autoaggressive Destructive Behavior Among Children and Adolescents: a Brief Overview
    Korlan Saduakassova, Mukhit Zhanuzakov, Gulzhan Kassenova, V.V. Serbin · 2024 · Journal of Clinical Medicine of Kazakhstan
33. Psychiatric Disorders and Adverse Life Events as Suicidality Predictors in Disadvantaged Youth from a Leading Institution of Mental Health: A Machine Learning Approach
    María Elena Márquez-Caraveo, Blanca Estela Barcelata Eguiarte, Hortensia Moreno-Macías, Chiharu Murata, Verónica Pérez-Barrón · 2025 · Research Square
34. Developing a suicide risk prediction model for hospitalized adolescents with depression in China
    Juan Zhao, Ying Li, Yangjie Chen, Ahmad Naqib Shuid · 2025 · Frontiers in Psychiatry
35. The concise machine learning prediction models for suicide attempt in China: Based on demographic and social factors
    Juncheng Lyu, Chao Wang, Zhe Gao, Jie Zhang · 2025 · Journal of Affective Disorders
36. Exploring Suicidal Ideation Predictors in U.S. Adults with Depression: The Roles of Demographics and Vitamin D in a Clinical Study
    Hong Guo, Lu Dai, Jian Zhan · 2025 · Research Square
37. Comparison of factors distinguishing suicide attempters among suicidal ideators and factors associated with suicidal ideation and attempts using machine learning models in South Korean adolescents
    HoJung Ahn, Hae‐Young Kim, Ki-Bong Choi, Hye-Jeong Jo, Sunmi Song, Junesun Kim · 2025 · Journal of Psychiatric Research
38. Optimizing Suicide Risk Prediction in Korea: A Comparison of Model Performance Using Resampling Methods and Machine Learning Algorithms
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39. Detecting Undiagnosed Mental Health Conditions Using Positive and Unlabeled Learning: Identifying Uncoded Self-Harm in Veterans’ Electronic Health Records (Preprint)
    Praveen Kumar, Alexandria D. Viszolay, Rajesh Upadhayaya, Fariha Moomtaheen, Donald R. Greer, Cristian Bologa, Kristan Alexander Schneider, Sharon E. Davis, Michael Edwin Matheny, David N. van der Goes et al. · 2025
40. Additional file 2 of Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods
    Bull, Lucy M., Mark Lunt, Glen Philip Martin, Kimme Leanne Hyrich, Jamie C. Sergeant · 2020 · Figshare
41. Additional file 1 of Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study
    Zheng, Shuqiong, Weixiong Zeng, Q Q Xin, Youran Ye, Xiang Xue, Li, Enze, Liu, Ting, Na Yan, Chen, Weiguo, Honglei Yin · 2022 · Figshare
42. An Explainable Triple‐Layered Ensemble Model for Early Prediction of Suicide Risk Using Machine Learning
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43. Investigating acceptance of current and future artificial intelligence systems for suicide prevention
    Jolene A. Cox, Brianna E. Ivory, Paul M. Salmon, Gemma J. M. Read · 2026 · AI & Society
44. Méthodes de fouille de données en épidémiologie psychiatrique : application à l’analyse des facteurs et marqueurs de risque de la symptomatologie dépressive à l’adolescence.
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45. Uncovering the determinants of infant and toddler childcare demand in less-developed rural China: a machine learning perspective
    Tingting Fan, Jichang Guo · 2026 · Frontiers in Public Health
46. Detecting Uncoded Self-Harm in Veterans’ Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study
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47. Predicting adolescent depression: an interpretable machine learning model
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48. Machine learning-based predictive factor analysis of depression among Chinese adolescents
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49. Predicting Next‐Day Passive Suicidal Ideation in At‐Risk Youth
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50. A Local Outpatient Practice-Level Prediction Model for Short-Term Psychiatric Emergency Presentation
    John L. Havlik, Ben D. Tyrrell, Jeanette X. Polaschek, Nathan Bell, Eric R. Arzubi · 2026 · medRxiv
51. An Exploratory Study of Social Skills Deficits and Suicide Attempts in Adolescent Psychiatric Inpatients: A Machine Learning Analysis of Goldstein’s ART Framework
    Przemyslaw Temistokles Zakowicz, Monika Szewczuk-Bogusławska, Maksymilian Aleksander Brzezicki, Tomasz Strawczyński, Maria Skibińska, Barbara Remberk · 2026 · Journal of Clinical Medicine
52. Large language models in adolescent suicide prevention: from language signals to accountable action
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53. Machine Learning Prediction of First Suicide Attempts in Early Adolescence Among Child Ideators
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54. An Explainable Machine Learning Approach to Predict Suicidal Thoughts in University Students: The SMS‐ME Project
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55. A neural network model for recognizing indicators of presuicidal risk in adolescents in the digital educational environment
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56. Artificial Intelligence and Psychiatric Training: Opportunities, Challenges, and the Future of Mental Health Education
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57. Machine Learning for Suicidality and Depression Risk Prediction: A Systematic Review of Electronic Health Records, Social Media, and Wearable Sensors
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58. Risk Alert System to Facilitate Universal Suicide Risk Screening in Emergency Care: Protocol for a Prediction Model and Software Development Study Using a Co-Design Approach
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59. A machine learning framework for the multi-class prediction of suicide attempt risk
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60. EHR-based machine learning for psychiatric outcomes in youth and young adults: Challenges and opportunities.
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61. Development and Internal Validation of an Interpretable Machine Learning Model for Identifying Past-Year Nonsuicidal Self-Injury Among Adolescents With Depression: Retrospective Study
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64. A Scoping Review Evaluating Artificial Intelligence Applications Across Various Injury Prevention Domains
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