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Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV.
Sadeghi, Sina; Hempel, Lars; Rodemund, Niklas; Kirsten, Toralf.
Afiliación
  • Sadeghi S; Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany. sina.sadeghi@medizin.uni-leipzig.de.
  • Hempel L; Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany. sina.sadeghi@medizin.uni-leipzig.de.
  • Rodemund N; Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany.
  • Kirsten T; Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany.
Sci Rep ; 14(1): 11438, 2024 05 20.
Article en En | MEDLINE | ID: mdl-38763952
ABSTRACT
The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Bases de Datos Factuales / Cuidados Críticos / Unidades de Cuidados Intensivos Límite: Adolescent / Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Bases de Datos Factuales / Cuidados Críticos / Unidades de Cuidados Intensivos Límite: Adolescent / Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido