Detection of pharmacovigilance-related adverse events using electronic health records and automated methods.
Clin Pharmacol Ther
; 92(2): 228-34, 2012 Aug.
Article
em En
| MEDLINE
| ID: mdl-22713699
ABSTRACT
Electronic health records (EHRs) are an important source of data for detection of adverse drug reactions (ADRs). However, adverse events are frequently due not to medications but to the patients' underlying conditions. Mining to detect ADRs from EHR data must account for confounders. We developed an automated method using natural-language processing (NLP) and a knowledge source to differentiate cases in which the patient's disease is responsible for the event rather than a drug. Our method was applied to 199,920 hospitalization records, concentrating on two serious ADRs rhabdomyolysis (n = 687) and agranulocytosis (n = 772). Our method automatically identified 75% of the cases, those with disease etiology. The sensitivity and specificity were 93.8% (confidence interval 88.9-96.7%) and 91.8% (confidence interval 84.0-96.2%), respectively. The method resulted in considerable saving of time for every 1 h spent in development, there was a saving of at least 20 h in manual review. The review of the remaining 25% of the cases therefore became more feasible, allowing us to identify the medications that had caused the ADRs.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Sistemas de Notificação de Reações Adversas a Medicamentos
/
Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos
/
Registros Eletrônicos de Saúde
/
Farmacovigilância
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Clin Pharmacol Ther
Ano de publicação:
2012
Tipo de documento:
Article
País de afiliação:
Estados Unidos