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

Theme issue 2019: Semantics of Mental Health (5) Decision Support for Health Professionals (1185) Machine Learning (1465) Natural Language Processing (692) Depression and Mood Disorders; Suicide Prevention (1363) Diagnostic Tools in Mental Health (242) Intentional Self-Harm (96) Methods and New Tools in Mental Health Research (454)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17784, first published .
Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach

Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach

Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach

Authors of this article:

Jihad S Obeid1 Author Orcid Image ;   Jennifer Dahne1 Author Orcid Image ;   Sean Christensen1 Author Orcid Image ;   Samuel Howard1 Author Orcid Image ;   Tami Crawford1 Author Orcid Image ;   Lewis J Frey1 Author Orcid Image ;   Tracy Stecker1 Author Orcid Image ;   Brian E Bunnell2 Author Orcid Image
Article Authors Cited by (22) Tweetations (6) Metrics

Journals

  1. Mann J, Michel C, Auerbach R. Improving Suicide Prevention Through Evidence-Based Strategies: A Systematic Review. American Journal of Psychiatry 2021;178(7):611 View
  2. Hsu J, Hsu T, Hsieh C, Singaravelan A. Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records. Sensors 2020;20(24):7116 View
  3. Wang H, Avillach P. Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning. JMIR Medical Informatics 2021;9(4):e24754 View
  4. Boggs J, Kafka J. A Critical Review of Text Mining Applications for Suicide Research. Current Epidemiology Reports 2022;9(3):126 View
  5. Rozova V, Witt K, Robinson J, Li Y, Verspoor K. Detection of self-harm and suicidal ideation in emergency department triage notes. Journal of the American Medical Informatics Association 2022;29(3):472 View
  6. Rathnayaka P, Mills N, Burnett D, De Silva D, Alahakoon D, Gray R. A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring. Sensors 2022;22(10):3653 View
  7. Hoekstra O, Hurst W, Tummers J. Healthcare related event prediction from textual data with machine learning: A Systematic Literature Review. Healthcare Analytics 2022;2:100107 View
  8. Young J, Bishop S, Humphrey C, Pavlacic J. A review of natural language processing in the identification of suicidal behavior. Journal of Affective Disorders Reports 2023;12:100507 View
  9. Huang S, Lewis M, Bao Y, Adekkanattu P, Adkins L, Banerjee S, Bian J, Gellad W, Goodin A, Luo Y, Fairless J, Walunas T, Wilson D, Wu Y, Yin P, Oslin D, Pathak J, Lo-Ciganic W. Predictive Modeling for Suicide-Related Outcomes and Risk Factors among Patients with Pain Conditions: A Systematic Review. Journal of Clinical Medicine 2022;11(16):4813 View
  10. Sheu Y, Sun J, Lee H, Castro V, Barak-Corren Y, Song E, Madsen E, Gordon W, Kohane I, Churchill S, Reis B, Cai T, Smoller J. An efficient landmark model for prediction of suicide attempts in multiple clinical settings. Psychiatry Research 2023;323:115175 View
  11. Mann J, Michel C, Auerbach R. Improving Suicide Prevention Through Evidence-Based Strategies: A Systematic Review. Focus 2023;21(2):182 View
  12. Workman T, Goulet J, Brandt C, Warren A, Eleazer J, Skanderson M, Lindemann L, Blosnich J, O'Leary J, Zeng‐Treitler Q. Identifying suicide documentation in clinical notes through zero‐shot learning. Health Science Reports 2023;6(9) View
  13. Masud J, Kuo C, Yeh C, Yang H, Lin M. Applying Deep Learning Model to Predict Diagnosis Code of Medical Records. Diagnostics 2023;13(13):2297 View
  14. Sim J, Huang X, Horan M, Stewart C, Robison L, Hudson M, Baker J, Huang I. Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review. Artificial Intelligence in Medicine 2023;146:102701 View
  15. Arora A, Bojko L, Kumar S, Lillington J, Panesar S, Petrungaro B. Assessment of machine learning algorithms in national data to classify the risk of self-harm among young adults in hospital: A retrospective study. International Journal of Medical Informatics 2023;177:105164 View
  16. Rohanian O, Nouriborji M, Jauncey H, Kouchaki S, Nooralahzadeh F, Clifton L, Merson L, Clifton D. Lightweight transformers for clinical natural language processing. Natural Language Engineering 2024;30(5):887 View
  17. 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
  18. Aden I, Child C, Reyes-Aldasoro C. International Classification of Diseases Prediction from MIMIIC-III Clinical Text Using Pre-Trained ClinicalBERT and NLP Deep Learning Models Achieving State of the Art. Big Data and Cognitive Computing 2024;8(5):47 View
  19. 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

Books/Policy Documents

  1. Martínez-Castaño R, Htait A, Azzopardi L, Moshfeghi Y. Experimental IR Meets Multilinguality, Multimodality, and Interaction. View
  2. Martínez-Castaño R, Htait A, Azzopardi L, Moshfeghi Y. Early Detection of Mental Health Disorders by Social Media Monitoring. View
  3. Chaki J. Next Generation Healthcare Informatics. View