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

Digital Biomarkers and Digital Phenotyping (178) Machine Learning (1385) Depression and Mood Disorders; Suicide Prevention (1282) Diagnostic Tools in Mental Health (222) Mobile Health in Psychiatry (401)

Published on in Vol 8, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15901, first published .
Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study

Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study

Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study

Authors of this article:

Alina Haines-Delmont1 Author Orcid Image ;   Gurdit Chahal2 Author Orcid Image ;   Ashley Jane Bruen3 Author Orcid Image ;   Abbie Wall3 Author Orcid Image ;   Christina Tara Khan4 Author Orcid Image ;   Ramesh Sadashiv2 Author Orcid Image ;   David Fearnley5 Author Orcid Image
Article Authors Cited by (31) Tweetations (13) Metrics

Journals

  1. D’Hotman D, Loh E. AI enabled suicide prediction tools: a qualitative narrative review. BMJ Health & Care Informatics 2020;27(3):e100175 View
  2. Bruen A, Wall A, Haines-Delmont A, Perkins E. Exploring Suicidal Ideation Using an Innovative Mobile App-Strength Within Me: The Usability and Acceptability of Setting up a Trial Involving Mobile Technology and Mental Health Service Users. JMIR Mental Health 2020;7(9):e18407 View
  3. Balcombe L, De Leo D. Digital Mental Health Challenges and the Horizon Ahead for Solutions. JMIR Mental Health 2021;8(3):e26811 View
  4. Lee J, Turchioe M, Creber R, Biviano A, Hickey K, Bakken S. Phenotypes of engagement with mobile health technology for heart rhythm monitoring. JAMIA Open 2021;4(2) View
  5. Sels L, Homan S, Ries A, Santhanam P, Scheerer H, Colla M, Vetter S, Seifritz E, Galatzer-Levy I, Kowatsch T, Scholz U, Kleim B. SIMON: A Digital Protocol to Monitor and Predict Suicidal Ideation. Frontiers in Psychiatry 2021;12 View
  6. Braciszewski J. Digital Technology for Suicide Prevention. Advances in Psychiatry and Behavioral Health 2021;1(1):53 View
  7. Balcombe L, De Leo D. The Potential Impact of Adjunct Digital Tools and Technology to Help Distressed and Suicidal Men: An Integrative Review. Frontiers in Psychology 2022;12 View
  8. Gupta M, Ramar D, Vijayan R, Gupta N. Artificial Intelligence Tools for Suicide Prevention in Adolescents and Young Adults. Adolescent Psychiatry 2022;12(1):1 View
  9. Shoib S, Chandradasa M, Nahidi M, Amanda T, Khan S, Saeed F, Swed S, Mazza M, Di Nicola M, Martinotti G, Di Giannantonio M, Armiya’u A, De Berardis D. Facebook and Suicidal Behaviour: User Experiences of Suicide Notes, Live-Streaming, Grieving and Preventive Strategies—A Scoping Review. International Journal of Environmental Research and Public Health 2022;19(20):13001 View
  10. Horwitz A, Czyz E, Al-Dajani N, Dempsey W, Zhao Z, Nahum-Shani I, Sen S. Utilizing daily mood diaries and wearable sensor data to predict depression and suicidal ideation among medical interns. Journal of Affective Disorders 2022;313:1 View
  11. Milne-Ives M, Selby E, Inkster B, Lam C, Meinert E, Narasimhan P. Artificial intelligence and machine learning in mobile apps for mental health: A scoping review. PLOS Digital Health 2022;1(8):e0000079 View
  12. Mendes J, Moura I, Van de Ven P, Viana D, Silva F, Coutinho L, Teixeira S, Rodrigues J, Teles A. Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review. Journal of Medical Internet Research 2022;24(2):e28735 View
  13. Adler D, Wang F, Mohr D, Choudhury T, Chen C. Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies. PLOS ONE 2022;17(4):e0266516 View
  14. Zeng X, Linwood S, Liu C. Pretrained transformer framework on pediatric claims data for population specific tasks. Scientific Reports 2022;12(1) View
  15. Van Assche E, Antoni Ramos-Quiroga J, Pariante C, Sforzini L, Young A, Flossbach Y, Gold S, Hoogendijk W, Baune B, Maron E. Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art. European Neuropsychopharmacology 2022;60:100 View
  16. Salditt M, Humberg S, Nestler S. Gradient Tree Boosting for Hierarchical Data. Multivariate Behavioral Research 2023;58(5):911 View
  17. Bertl M, Ross P, Draheim D. A survey on AI and decision support systems in psychiatry – Uncovering a dilemma. Expert Systems with Applications 2022;202:117464 View
  18. Diniz E, Fontenele J, de Oliveira A, Bastos V, Teixeira S, Rabêlo R, Calçada D, dos Santos R, de Oliveira A, Teles A. Boamente: A Natural Language Processing-Based Digital Phenotyping Tool for Smart Monitoring of Suicidal Ideation. Healthcare 2022;10(4):698 View
  19. Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. Journal of Biomedical Informatics 2023;138:104278 View
  20. Winkler T, Büscher R, Larsen M, Kwon S, Torous J, Firth J, Sander L. Passive Sensing in the Prediction of Suicidal Thoughts and Behaviors: Protocol for a Systematic Review. JMIR Research Protocols 2022;11(11):e42146 View
  21. De Boer C, Ghomrawi H, Zeineddin S, Linton S, Kwon S, Abdullah F. A Call to Expand the Scope of Digital Phenotyping. Journal of Medical Internet Research 2023;25:e39546 View
  22. Kleiman E, Glenn C, Liu R. The use of advanced technology and statistical methods to predict and prevent suicide. Nature Reviews Psychology 2023;2(6):347 View
  23. Sedlakova J, Daniore P, Horn Wintsch A, Wolf M, Stanikic M, Haag C, Sieber C, Schneider G, Staub K, Alois Ettlin D, Grübner O, Rinaldi F, von Wyl V, Sarmiento R. Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review. PLOS Digital Health 2023;2(10):e0000347 View
  24. Barrigon M, Romero-Medrano L, Moreno-Muñoz P, Porras-Segovia A, Lopez-Castroman J, Courtet P, Artés-Rodríguez A, Baca-Garcia E. One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study. Journal of Medical Internet Research 2023;25:e43719 View
  25. Czyz E, King C, Al-Dajani N, Zimmermann L, Hong V, Nahum-Shani I. Ecological Momentary Assessments and Passive Sensing in the Prediction of Short-Term Suicidal Ideation in Young Adults. JAMA Network Open 2023;6(8):e2328005 View
  26. Tio E, Misztal M, Felsky D. Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning. Frontiers in Psychiatry 2024;14 View
  27. Bryan A, Heinz M, Salzhauer A, Price G, Tlachac M, Jacobson N. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials & Devices 2024;2(2):778 View
  28. Pigoni A, Delvecchio G, Turtulici N, Madonna D, Pietrini P, Cecchetti L, Brambilla P. Machine learning and the prediction of suicide in psychiatric populations: a systematic review. Translational Psychiatry 2024;14(1) View
  29. Jyoti A, Yadav V, Pal A, Rahul M, Jha S. The Transformative Impact of AI and Machine Learning on Human Psychology. Recent Advances in Computer Science and Communications 2024;17(2) View

Books/Policy Documents

  1. Chen X, Genc Y. Artificial Intelligence in HCI. View