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Identifying and Predicting Postoperative Infections Based on Readily Available Electronic Health Record Data.
van der Meijden, Siri Lise; van Boekel, Anna; Schinkelshoek, Laurens; van Goor, Harry; de Boer, Mark; Steyerberg, Ewout; Geerts, Bart; Arbous, Sesmu.
Afiliação
  • van der Meijden SL; Healthplus.ai, Amsterdam, The Netherlands.
  • van Boekel A; Leiden University Medical Center, Leiden, The Netherlands.
  • Schinkelshoek L; Leiden University Medical Center, Leiden, The Netherlands.
  • van Goor H; Healthplus.ai, Amsterdam, The Netherlands.
  • de Boer M; Radboud University Medical Center, Nijmegen, The Netherlands.
  • Steyerberg E; Leiden University Medical Center, Leiden, The Netherlands.
  • Geerts B; Leiden University Medical Center, Leiden, The Netherlands.
  • Arbous S; Healthplus.ai, Amsterdam, The Netherlands.
Stud Health Technol Inform ; 302: 348-349, 2023 May 18.
Article em En | MEDLINE | ID: mdl-37203678
ABSTRACT
Identification of postoperative infections based on retrospective patient data is currently done using manual chart review. We used a validated, automated labelling method based on registrations and treatments to develop a high-quality prediction model (AUC 0.81) for postoperative infections.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Complicações Pós-Operatórias / Registros Eletrônicos de Saúde Tipo de estudo: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Complicações Pós-Operatórias / Registros Eletrônicos de Saúde Tipo de estudo: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article