This paper is in the following e-collection/theme issue:

Artificial Intelligence (2129) mHealth for Wellness, Behavior Change and Prevention (3315) Depression and Mood Disorders; Suicide Prevention (1691) Methods and New Tools in Mental Health Research (558) Mobile Health (mhealth) (3464) New Methods (1134) Diagnostic Tools in Mental Health (326) Theme Issue: Bayesian Methods In Medical Research (30) Machine Learning (2076) Knowledge and Attitudes of Mental Health Professionals towards Digital Interventions (37)

Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24560, first published .
Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations

Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations

Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations

Authors of this article:

Stephane Mouchabac1, 2 Author Orcid Image ;   Philippe Leray3 Author Orcid Image ;   Vladimir Adrien1, 2 Author Orcid Image ;   Fanny Gollier-Briant4 Author Orcid Image ;   Olivier Bonnot4, 5 Author Orcid Image
Article Authors Cited by (16) Tweetations (27) Metrics

Journals

  1. Bonnot O, Adrien V, Venelle V, Bonneau D, Gollier-Briant F, Mouchabac S. Mobile App for Parental Empowerment for Caregivers of Children With Autism Spectrum Disorders: Prospective Open Trial. JMIR Mental Health 2021;8(9):e27803 View
  2. Cohen D, Mouchabac S. IA et psychiatrie enfant et adulte. Bulletin de l'Académie Nationale de Médecine 2022;206(8):1128 View
  3. Yang Y, Xu F, Chen J, Tao C, Li Y, Chen Q, Tang S, Lee H, Shen W. Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review. Biosensors and Bioelectronics 2023;229:115233 View
  4. Pichikov A, Popov Y. Problems with Suicidal Behavior Prevention in Adolescents: a Narrative Literature Review. Consortium Psychiatricum 2022;3(2):5 View
  5. Mouchabac S, Conejero I, Lakhlifi C, Msellek I, Malandain L, Adrien V, Ferreri F, Millet B, Bonnot O, Bourla A, Maatoug R. Improving clinical decision-making in psychiatry: implementation of digital phenotyping could mitigate the influence of patient’s and practitioner’s individual cognitive biases. Dialogues in Clinical Neuroscience 2021;23(1):52 View
  6. Huang Q, Lin S, Li Y, Huang S, Liao Z, Chen X, Shao T, Li Y, Cai Y, Qi J, Shen H. Suicidal Ideation Is Associated With Excessive Smartphone Use Among Chinese College Students. Frontiers in Public Health 2022;9 View
  7. Aoun J, Spodenkiewicz M, Marimoutou C. Scoping review on prevention of suicidal thoughts and behaviors in adolescents: methods, effectiveness and future directions. Frontiers in Child and Adolescent Psychiatry 2024;3 View
  8. Atmakuru A, Shahini A, Chakraborty S, Seoni S, Salvi M, Hafeez-Baig A, Rashid S, Tan R, Barua P, Molinari F, Acharya U. Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023). Information Fusion 2025;114:102673 View
  9. Creemers J, Textor J. Leveraging mathematical models to improve the statistical robustness of cancer immunotherapy trials. Current Opinion in Systems Biology 2025;40:100540 View
  10. Babakov N, Sivaprasad A, Reiter E, Bugarín-Diz A. Reusability of Bayesian Networks case studies: a survey. Applied Intelligence 2025;55(6) View
  11. He W, Tang D, Jin Y, Zhang W, Kang Y, Xia Q. Quality of cerebral palsy videos on Chinese social media platforms. Scientific Reports 2025;15(1) View
  12. Sharma G, Yaffe M, Ghadiri P, Gandhi R, Pinkham L, Gore G, Abbasgholizadeh-Rahimi S. Use of Artificial Intelligence in Adolescents’ Mental Health Care: Systematic Scoping Review of Current Applications and Future Directions. JMIR Mental Health 2025;12:e70438 View
  13. Li Y, Zou K, Wang Y, Zhang Y, Zhong J, Zhou W, Tang F, Peng L, Liu X, Deng L. Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques. BMC Medical Informatics and Decision Making 2025;25(1) View
  14. Liu L, Li Z, Hu Y, Li C, He S, Zhang S, Gao J, Zhu H, Huang G. Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2025;27:e73052 View

Books/Policy Documents

  1. Reen J, Friday A, Orji R. Persuasive Technology. View
  2. Benikhlef S, Leray P, Raschia G, Messaoud M, Sakly F. Symbolic and Quantitative Approaches to Reasoning with Uncertainty. View