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Published on in Vol 11 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53366, first published .
Doctor's stethoscope, laptop, and ECG chart with pills on mint background

Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study

Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study

Journals

  1. Gabriel R, Park B, Hsu C, Macias A. A Review of Leveraging Artificial Intelligence to Predict Persistent Postoperative Opioid Use and Opioid Use Disorder and its Ethical Considerations. Current Pain and Headache Reports 2025;29(1) View
  2. Mahbub M, Dams G, Srinivasan S, Rizy C, Danciu I, Trafton J, Knight K. Decoding substance use disorder severity from clinical notes using a large language model. npj Mental Health Research 2025;4(1) View
  3. Hurley R, Bland K, Chaskes M, Hill E, Adams M. Diagnosis and coding of opioid misuse: a systematic scoping review and implementation framework. Pain Medicine 2025;26(7):372 View
  4. Coleman B, Corcoran K, Brandt C, Goulet J, Luther S, Lisi A. Identifying Patient-Reported Outcome Measure Documentation in Veterans Health Administration Chiropractic Clinic Notes: Natural Language Processing Analysis. JMIR Medical Informatics 2025;13:e66466 View
  5. Zompola A, Asimakopoulos T, Iosifidou C, Monopatis D, Braimakis F, Kouroukli I. Machine Learning Applications for Opioid Use Management in Chronic Cancer Pain: A Systematic Scoping Review. Cureus 2026 View
  6. Black T. A Review of Performance and Integration of Artificial Intelligence Case-Finding Tools for Psychiatric Illness Within Health Systems. Psychiatric Annals 2026;56(4) View
  7. Trujeque J, Simonetti J, Ortiz I, Ingraham N, Mesfin N, Yeung J, Zhang R, Brenner L, Dudley R. Natural Language Processing Identification of Nonprescribed Fentanyl Use in Electronic Health Records: Algorithm Development and Validation Study. Journal of Medical Internet Research 2026;28:e97485 View