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Learning and Predicting from Dynamic Models for COVID-19 Patient Monitoring.
Wang, Zitong; Bowring, Mary Grace; Rosen, Antony; Garibaldi, Brian; Zeger, Scott; Nishimura, Akihiko.
Afiliación
  • Wang Z; Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
  • Bowring MG; Departments of Biomedical Engineering and Biostatistics, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Rosen A; The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Garibaldi B; Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Zeger S; Department of Biostatistics and Medicine, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
  • Nishimura A; Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
Stat Sci ; 37(2): 251-265, 2022 May.
Article en En | MEDLINE | ID: mdl-37213435
COVID-19 has challenged health systems to learn how to learn. This paper describes the context, methods and challenges for learning to improve COVID-19 care at one academic health center. Challenges to learning include: (1) choosing a right clinical target; (2) designing methods for accurate predictions by borrowing strength from prior patients' experiences; (3) communicating the methodology to clinicians so they understand and trust it; (4) communicating the predictions to the patient at the moment of clinical decision; and (5) continuously evaluating and revising the methods so they adapt to changing patients and clinical demands. To illustrate these challenges, this paper contrasts two statistical modeling approaches - prospective longitudinal models in common use and retrospective analogues complementary in the COVID-19 context - for predicting future biomarker trajectories and major clinical events. The methods are applied to and validated on a cohort of 1,678 patients who were hospitalized with COVID-19 during the early months of the pandemic. We emphasize graphical tools to promote physician learning and inform clinical decision making.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Stat Sci Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Stat Sci Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos