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PDSS: A Pharmacological Decision Support System for Diabetics Patients with COVID-19.
Amaya-Rodriguez, Isabel; Larburu, Nekane; Martinez-Herrera, María Rollán; Rebescher, Kristin; Macia, Iván; Armengol De La Hoz, Miguel A; Rubio-Escudero, Cristina; Garin-Muga, Alba.
Afiliação
  • Amaya-Rodriguez I; Department of Computer Science, University of Seville, Spain.
  • Larburu N; Biodonostia Health Research Institute, (Bioengineering Area), eHealth Group, San Sebastián 20014, Spain.
  • Martinez-Herrera MR; Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Rebescher K; Biodonostia Health Research Institute, (Bioengineering Area), eHealth Group, San Sebastián 20014, Spain.
  • Macia I; Biodonostia Health Research Institute, (Bioengineering Area), eHealth Group, San Sebastián 20014, Spain.
  • Armengol De La Hoz MA; Big Data Department - Regional Ministry of Health of Southern Spain, Spain.
  • Rubio-Escudero C; Department of Computer Science, University of Seville, Spain.
  • Garin-Muga A; Biodonostia Health Research Institute, (Bioengineering Area), eHealth Group, San Sebastián 20014, Spain.
Stud Health Technol Inform ; 302: 521-525, 2023 May 18.
Article em En | MEDLINE | ID: mdl-37203740
ABSTRACT
With the advent of SARS-CoV-2, several studies have shown that there is a higher mortality rate in patients with diabetes and, in some cases, it is one of the side effects of overcoming the disease. However, there is no clinical decision support tool or specific treatment protocols for these patients. To tackle this issue, in this paper we present a Pharmacological Decision Support System (PDSS) providing intelligent decision support for COVID-19 diabetic patient treatment selection, based on an analysis of risk factors with data from electronic medical records using Cox regression. The goal of the system is to create real world evidence including the ability to continuously learn to improve clinical practice and outcomes of diabetic patients with COVID-19.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Diabetes Mellitus / COVID-19 Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Diabetes Mellitus / COVID-19 Idioma: En Ano de publicação: 2023 Tipo de documento: Article