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

Artificial Intelligence (1371) Machine Learning (1465) Natural Language Processing (693) Innovative Methods in Public Health and Surveillance (332) Opioid and Related Substance Misuse (147) Substance Abuse (419)

Published on in Vol 22, No 1 (2020): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15645, first published .
The Detection of Opioid Misuse and Heroin Use From Paramedic Response Documentation: Machine Learning for Improved Surveillance

The Detection of Opioid Misuse and Heroin Use From Paramedic Response Documentation: Machine Learning for Improved Surveillance

The Detection of Opioid Misuse and Heroin Use From Paramedic Response Documentation: Machine Learning for Improved Surveillance

Authors of this article:

José Tomás Prieto1, 1 Author Orcid Image ;   Kenneth Scott2 Author Orcid Image ;   Dean McEwen2 Author Orcid Image ;   Laura J Podewils2 Author Orcid Image ;   Alia Al-Tayyib2, 3 Author Orcid Image ;   James Robinson4 Author Orcid Image ;   David Edwards4 Author Orcid Image ;   Seth Foldy2, 3, 5 Author Orcid Image ;   Judith C Shlay2, 5 Author Orcid Image ;   Arthur J Davidson2, 5, 6 Author Orcid Image
Article Authors Cited by (23) Tweetations (28) Metrics

Journals

  1. Barenholtz E, Fitzgerald N, Hahn W. Machine-learning approaches to substance-abuse research: emerging trends and their implications. Current Opinion in Psychiatry 2020;33(4):334 View
  2. Mortaz E, Dag A, Hutzler L, Gharibo C, Anzisi L, Bosco J. Short-term prediction of opioid prescribing patterns for orthopaedic surgical procedures: a machine learning framework. Journal of Business Analytics 2021;4(1):1 View
  3. Balsamo D, Bajardi P, Salomone A, Schifanella R. Patterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions. Journal of Medical Internet Research 2021;23(1):e21212 View
  4. De Silva K, Mathews N, Teede H, Forbes A, Jönsson D, Demmer R, Enticott J. Clinical notes as prognostic markers of mortality associated with diabetes mellitus following critical care: A retrospective cohort analysis using machine learning and unstructured big data. Computers in Biology and Medicine 2021;132:104305 View
  5. Scott E, Hirabayashi L, Levenstein A, Krupa N, Jenkins P. The development of a machine learning algorithm to identify occupational injuries in agriculture using pre-hospital care reports. Health Information Science and Systems 2021;9(1) View
  6. Dong X, Deng J, Hou W, Rashidian S, Rosenthal R, Saltz M, Saltz J, Wang F. Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning. Journal of Biomedical Informatics 2021;116:103725 View
  7. Hallowell B, Chambers L, Rhodes J, Basta M, Viner-Brown S, Lasher L. Using Emergency Medical Services Data to Monitor Nonfatal Opioid Overdoses in Real Time. Public Health Reports® 2021;136(1_suppl):40S View
  8. Arifkhanova A, Prieto J, Davidson A, Al-Tayyib A, Hawkins E, Kraus E, McEwen D, Podewils L, Foldy S, Gillespie E, Taub J, Shlay J. Defining Opioid-related Problems Using a Health Care Safety Net Institution’s Inpatient Electronic Health Records: Limitations of Diagnosis-based Definitions. Journal of Addiction Medicine 2022 View
  9. Cash R, Richards C. Emergency Medical Services Data: An Unexpected Source of Variation in Stroke Care Performance. Journal of the American Heart Association 2023;12(1) View
  10. Gadhia S, Richards G, Marriott T, Rose J. Artificial intelligence and opioid use: a narrative review. BMJ Innovations 2023;9(2):78 View
  11. Sivaraman J, Proescholdbell S, Ezzell D, Shanahan M. Characterizing Opioid Overdoses Using Emergency Medical Services Data. Public Health Reports® 2021;136(1_suppl):62S View
  12. Omranian S, Zolnoori M, Huang M, Campos-Castillo C, McRoy S. Predicting Patient Satisfaction With Medications for Treating Opioid Use Disorder: Case Study Applying Natural Language Processing to Reviews of Methadone and Buprenorphine/Naloxone on Health-Related Social Media. JMIR Infodemiology 2023;3:e37207 View
  13. Hasan M, Young G, Patel M, Modestino A, Sanchez L, Noor-E-Alam M. A machine learning framework to predict the risk of opioid use disorder. Machine Learning with Applications 2021;6:100144 View
  14. Cresta Morgado P, Carusso M, Alonso Alemany L, Acion L. Practical foundations of machine learning for addiction research. Part I. Methods and techniques. The American Journal of Drug and Alcohol Abuse 2022;48(3):260 View
  15. Poulsen M, Freda P, Troiani V, Davoudi A, Mowery D. Classifying Characteristics of Opioid Use Disorder From Hospital Discharge Summaries Using Natural Language Processing. Frontiers in Public Health 2022;10 View
  16. Wang H, Ng Q, Arulanandam S, Tan C, Ong M, Feng M. Building a Machine Learning-based Ambulance Dispatch Triage Model for Emergency Medical Services. Health Data Science 2023;3 View
  17. Garbin C, Marques N, Marques O. Machine learning for predicting opioid use disorder from healthcare data: A systematic review. Computer Methods and Programs in Biomedicine 2023;236:107573 View
  18. Fuhrer C. Intelligence Artificielle : que dit la recherche récente ? Une approche combinée bibliométrique et textuelle. Management & Avenir 2023;N° 137(5):89 View
  19. Asilkalkan A, Dag A, Simsek S, Aydas O, Kibis E, Delen D. Streamlining patients’ opioid prescription dosage: an explanatory bayesian model. Annals of Operations Research 2023 View
  20. Ezell J, Ajayi B, Parikh T, Miller K, Rains A, Scales D. Drug Use and Artificial Intelligence: Weighing Concerns and Possibilities for Prevention. American Journal of Preventive Medicine 2024;66(3):568 View
  21. Graham S, Shifflet S, Amjad M, Claborn K, Veerappampalayam Easwaramoorthy S. An interpretable machine learning framework for opioid overdose surveillance from emergency medical services records. PLOS ONE 2024;19(1):e0292170 View
  22. Al-Ghannam R, Ykhlef M, Al-Dossari H. Robust Drug Use Detection on X: Ensemble Method with a Transformer Approach. Arabian Journal for Science and Engineering 2024;49(9):12867 View

Books/Policy Documents

  1. Srinivasan V, Rosero E, Sengottuvelan P, Singhal A, Singh C, Mayakannan S. Artificial Intelligence‐Based System Models in Healthcare. View