Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 3 de 3
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Health Informatics J ; 23(4): 291-303, 2017 12.
Artigo em Inglês | MEDLINE | ID: mdl-27199298

RESUMO

A health record database contains structured data fields that identify the patient, such as patient ID, patient name, e-mail and phone number. These data are fairly easy to de-identify, that is, replace with other identifiers. However, these data also occur in fields with doctors' free-text notes written in an abbreviated style that cannot be analyzed grammatically. If we replace a word that looks like a name, but isn't, we degrade readability and medical correctness. If we fail to replace it when we should, we degrade confidentiality. We de-identified an existing Danish electronic health record database, ending up with 323,122 patient health records. We had to invent many methods for de-identifying potential identifiers in the free-text notes. The de-identified health records should be used with caution for statistical purposes because we removed health records that were so special that they couldn't be de-identified. Furthermore, we distorted geography by replacing zip codes with random zip codes.


Assuntos
Compreensão , Confiabilidade dos Dados , Registros Eletrônicos de Saúde/normas , Confidencialidade , Humanos , Países Baixos
2.
Angle Orthod ; 83(1): 3-9, 2013 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-22702958

RESUMO

OBJECTIVE: To compare the root development and the growth rate of the mandibular third molar (M3 inf) in individuals where the M3 inf erupted vs individuals exhibiting M3 inf impaction. MATERIALS AND METHODS: Serial standardized intraoral radiographs (Eggen technique) were taken annually of the mandibular third molar region from 132 subjects (71 male and 61 female) from 15 to 20 years of age. Based on the films, 264 lower third molars were classified into an eruption and an impaction group. Root development was recorded according to a quantitative method described by Haavikko (1970), and the eruption status was analyzed using logistic regression. RESULTS: In total, 155 (59%) of the M3 inf erupted, and 109 (41%) were impacted at age 20. In 44 (33%) patients both M3 inf were impacted, in 21 (16%) patients one tooth was erupted and the contralateral tooth impacted, and in 67 (51%) patients both M3 inf were erupted. The more mature a tooth was at age 15, the higher was the probability of eruption (odds ratio: 3.89, P < .001). The growth rate of the root development stage was statistically significantly associated with the probability of eruption (odds ratio: 10.50, P  =  .041). CONCLUSIONS: Delayed mandibular third molar root development is associated with impaction. Radiographs taken at age 15 may predict the risk of impaction and thereby guide decision making for the orthodontist or the oral and maxillofacial surgeon.


Assuntos
Dente Serotino/crescimento & desenvolvimento , Erupção Dentária/fisiologia , Raiz Dentária/crescimento & desenvolvimento , Dente Impactado/fisiopatologia , Adolescente , Fatores Etários , Feminino , Humanos , Análise dos Mínimos Quadrados , Modelos Logísticos , Estudos Longitudinais , Masculino , Mandíbula/diagnóstico por imagem , Dente Serotino/diagnóstico por imagem , Dente Serotino/fisiopatologia , Odontometria , Estudos Prospectivos , Radiografia Panorâmica , Fatores Sexuais , Raiz Dentária/diagnóstico por imagem , Dente Impactado/diagnóstico por imagem , Adulto Jovem
3.
Stud Health Technol Inform ; 169: 862-6, 2011.
Artigo em Inglês | MEDLINE | ID: mdl-21893869

RESUMO

Electronic health records (EHR) contain a large amount of structured data and free text. Exploring and sharing clinical data can improve healthcare and facilitate the development of medical software. However, revealing confidential information is against ethical principles and laws. We de-identified a Danish EHR database with 437,164 patients. The goal was to generate a version with real medical records, but related to artificial persons. We developed a de-identification algorithm that uses lists of named entities, simple language analysis, and special rules. Our algorithm consists of 3 steps: collect lists of identifiers from the database and external resources, define a replacement for each identifier, and replace identifiers in structured data and free text. Some patient records could not be safely de-identified, so the de-identified database has 323,122 patient records with an acceptable degree of anonymity, readability and correctness (F-measure of 95%). The algorithm has to be adjusted for each culture, language and database.


Assuntos
Registro Médico Coordenado/normas , Atenção Primária à Saúde/organização & administração , Algoritmos , Segurança Computacional , Confidencialidade , Dinamarca , Registros Eletrônicos de Saúde , Humanos , Registro Médico Coordenado/métodos , Sistemas de Identificação de Pacientes , Reconhecimento Automatizado de Padrão , Privacidade , Reprodutibilidade dos Testes , Medidas de Segurança , Software
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...