Using natural language processing to identify opioid use disorder in electronic health record data

Nursing and Health Professions

• Diseases 100%
• Opiate 100%
• Electronic Health Record 100%
• Natural Language Processing 100%
• Hospital 50%
• International Classification of Diseases 50%
• Algorithm 41%
• Prevalence 33%
• Sample 25%
• Diagnosis 16%
• Sensitivity and Specificity 16%
• Physician 8%
• Prevention 8%
• Emergency Ward 8%
• Prescription 8%
• Classification 8%
• Hospital Department 8%
• Addiction 8%
• Gold Standard 8%
• University Hospital 8%
• Adverse Outcome 8%
• Data Extraction 8%

Medicine and Dentistry

• Electronic Health Record 100%
• Opioid Abuse 100%
• Language Processing 100%
• International Classification of Diseases 54%
• Prevalence 36%
• Diagnosis 18%
• Sensitivity and Specificity 18%
• Physician 9%
• Patient 9%
• Opiate 9%
• Hospital 9%
• Emergency Department 9%
• Adverse Outcome 9%
• Epidemiologic Surveillance 9%
• Addiction Medicine 9%