Prevalence Estimation of Protected Health Information in Swedish Clinical Text.
Stud Health Technol Inform
; 235: 216-220, 2017.
Article
em En
| MEDLINE
| ID: mdl-28423786
ABSTRACT
Obscuring protected health information (PHI) in the clinical text of health records facilitates the secondary use of healthcare data in a privacy-preserving manner. Although automatic de-identification of clinical text using machine learning holds much promise, little is known about the relative prevalence of PHI in different types of clinical text and whether there is a need for domain adaptation when learning predictive models from one particular domain and applying it to another. In this study, we address these questions by training a predictive model and using it to estimate the prevalence of PHI in clinical text written (1) in different clinical specialties, (2) in different types of notes (i.e., under different headings), and (3) by persons in different professional roles. It is demonstrated that the overall PHI density is 1.57%; however, substantial differences exist across domains.
Palavras-chave
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Coleções:
01-internacional
Contexto em Saúde:
1_ASSA2030
Base de dados:
MEDLINE
Assunto principal:
Processamento de Linguagem Natural
/
Confidencialidade
/
Registros Eletrônicos de Saúde
Tipo de estudo:
Prevalence_studies
/
Prognostic_studies
/
Risk_factors_studies
Aspecto:
Patient_preference
Limite:
Humans
País/Região como assunto:
Europa
Idioma:
En
Revista:
Stud Health Technol Inform
Ano de publicação:
2017
Tipo de documento:
Article