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Integrating Clinical Knowledge and Real-World Evidence for Type 2 Diabetes Treatment.
Sun, Xingzhi; Zhao, Wei; Zuo, Lei; Dumitriu, Alexandra; Lee, Chuang-Chung; Cui, Nan; Liao, Xiyang; Zhao, Tingting; Jiang, Xuehan; Xu, Zhuoyang; Hu, Gang; Xie, Guotong; Wu, Hong; Huang, Yahua.
Afiliação
  • Sun X; Ping An Health Technology, Beijing, China.
  • Zhao W; Ping An Health Technology, Beijing, China.
  • Zuo L; Ping An Health Technology, Beijing, China.
  • Dumitriu A; Sanofi, Cambridge, Massachusetts, U.S.A.
  • Lee CC; Sanofi, Cambridge, Massachusetts, U.S.A.
  • Cui N; Sanofi (China) Investment, Shanghai, China.
  • Liao X; Ping An Health Technology, Beijing, China.
  • Zhao T; Ping An Health Technology, Beijing, China.
  • Jiang X; Ping An Health Technology, Beijing, China.
  • Xu Z; Ping An Health Technology, Beijing, China.
  • Hu G; Ping An Health Technology, Beijing, China.
  • Xie G; Ping An Health Technology, Beijing, China.
  • Wu H; Sanofi (China) Investment, Shanghai, China.
  • Huang Y; Sanofi (China) Investment, Shanghai, China.
AMIA Annu Symp Proc ; 2019: 838-847, 2019.
Article em En | MEDLINE | ID: mdl-32308880
Clinical decision support system (CDSS) plays a significant role nowadays and it assists physicians in making decisions for treatment. Generally based on clinical guideline, the principles of the recommendation are provided and may suggest several candidate medications for similar patient group with certain clinical conditions. However, it is challenging to prioritize these candidates and even refine the guideline to a finer level for patient-specific recommendation. Here we propose a method and system to integrate the clinical knowledge and real-world evidence (RWE) for type 2 diabetes treatment, to enable both standardized and personalized medication recommendation. The RWE is generated by medication effectiveness analysis and subgroup analysis. The knowledge model has been verified by clinical experts from the advanced hospitals. The data verification results show that the medications that are consistent with the method recommendation can lead to better clinical outcome in terms of glycemic control, compared to those inconsistent.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Quimioterapia Assistida por Computador / Medicina Baseada em Evidências / Sistemas de Apoio a Decisões Clínicas / Diabetes Mellitus Tipo 2 / Medicina de Precisão / Hipoglicemiantes Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Revista: AMIA Annu Symp Proc Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Quimioterapia Assistida por Computador / Medicina Baseada em Evidências / Sistemas de Apoio a Decisões Clínicas / Diabetes Mellitus Tipo 2 / Medicina de Precisão / Hipoglicemiantes Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Revista: AMIA Annu Symp Proc Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: China