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An Approach for Generating Realistic Australian Synthetic Healthcare Data.
Diouf, Ibrahima; Grimes, John; O'Brien, Mitchell J; Hassanzadeh, Hamed; Truran, Donna; Ngo, Hoa; Raniga, Parnesh; Lawley, Michael; Bauer, Denis C; Hansen, David; Khanna, Sankalp; Reguant, Roc.
Afiliação
  • Diouf I; Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Australia.
  • Grimes J; Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Australia.
  • O'Brien MJ; Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Australia.
  • Hassanzadeh H; Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Australia.
  • Truran D; Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Australia.
  • Ngo H; Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Australia.
  • Raniga P; Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Australia.
  • Lawley M; Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Australia.
  • Bauer DC; Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Australia.
  • Hansen D; Macquarie University, Department of Biomedical Sciences, Faculty of Medicine and Health Science, Macquarie Park, Australia.
  • Khanna S; Macquarie University, Applied BioSciences, Faculty of Science and Engineering, Macquarie Park, Australia.
  • Reguant R; Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Australia.
Stud Health Technol Inform ; 310: 820-824, 2024 Jan 25.
Article em En | MEDLINE | ID: mdl-38269923
ABSTRACT
Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient's anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Privacidade / Instalações de Saúde Limite: Humans País/Região como assunto: Oceania Idioma: En Revista: Stud Health Technol Inform Assunto da revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Austrália

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Privacidade / Instalações de Saúde Limite: Humans País/Região como assunto: Oceania Idioma: En Revista: Stud Health Technol Inform Assunto da revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Austrália