Automatically identifying opioid use disorder in non-cancer patients on chronic opioid therapy.
Health Informatics J
; 28(2): 14604582221107808, 2022.
Article
em En
| MEDLINE
| ID: mdl-35726687
ABSTRACT
Background:
Using the International Classification of Diseases (ICD) codes alone to record opioid use disorder (OUD) may not completely document OUD in the electronic health record (EHR). We developed and evaluated natural language processing (NLP) approaches to identify OUD from the clinal note. We explored the concordance between ICD-coded and NLP-identified OUD.Methods:
We studied EHRs from 13,654 (female 8223; male 5431) adult non-cancer patients who received chronic opioid therapy (COT) and had at least one clinical note between 2013 and 2018. Of eligible patients, we randomly selected 10,218 (75%) patients as the training set and the remaining 3436 patients (25%) as the test dataset for NLP approaches.Results:
We generated 539 terms representing OUD mentions in clinical notes (e.g., "opioid use disorder," "opioid abuse," "opioid dependence," "opioid overdose") and 73 terms representing OUD medication treatments. By domain expert manual review for the test dataset, our NLP approach yielded high performance 98.5% for precision, 100% for recall, and 99.2% for F-measure. The concordance of these NLP and ICD identified OUD was modest (Kappa = 0.63).Conclusions:
Our NLP approach can accurately identify OUD patients from clinical notes. The combined use of ICD diagnostic code and NLP approach can improve OUD identification.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Analgésicos Opioides
/
Transtornos Relacionados ao Uso de Opioides
Tipo de estudo:
Diagnostic_studies
/
Guideline
Limite:
Adult
/
Female
/
Humans
/
Male
Idioma:
En
Revista:
Health Informatics J
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos