Enhancing Risk Assessment in Patients Receiving Chronic Opioid Analgesic Therapy Using Natural Language Processing | Zendy

Irina V. Haller | Zendy; Colleen M. Renier | Zendy; Mitch Juusola | Zendy; Paul Hitz | Zendy; William M. Steffen | Zendy; Michael J. Asmus | Zendy; Terri Craig | Zendy; Jack Mardekian | Zendy; Elizabeth T. Masters | Zendy; Thomas E. Elliott | Zendy

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Enhancing Risk Assessment in Patients Receiving Chronic Opioid Analgesic Therapy Using Natural Language Processing

Author(s) -

Irina V. Haller,

Colleen M. Renier,

Mitch Juusola,

Paul Hitz,

William M. Steffen,

Michael J. Asmus,

Terri Craig,

Jack Mardekian,

Elizabeth T. Masters,

Thomas E. Elliott

Publication year - 2016

Publication title -

pain medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.893

H-Index - 97

eISSN - 1526-4637

pISSN - 1526-2375

DOI - 10.1093/pm/pnw283

Subject(s) - medicine , opioid , chronic pain , medical prescription , risk assessment , retrospective cohort study , natural history , intensive care medicine , psychiatry , pharmacology , receptor , computer security , computer science

Clinical guidelines for the use of opioids in chronic noncancer pain recommend assessing risk for aberrant drug-related behaviors prior to initiating opioid therapy. Despite recent dramatic increases in prescription opioid misuse and abuse, use of screening tools by clinicians continues to be underutilized. This research evaluated natural language processing (NLP) together with other data extraction techniques for risk assessment of patients considered for opioid therapy as a means of predicting opioid abuse.

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