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http://dx.doi.org/10.1080/08853126.1959.10380895 | DOI Listing |
J Med Case Rep
April 2023
Department of Psychiatry, Faculty of Medicine, Lebanese University, Hadat, Lebanon.
Stud Health Technol Inform
April 2018
Medexter Healthcare GmbH, Vienna, Austria.
Background: The diagnosis - and hence definitions - of healthcare-associated infections (HAIs) rely on microbiological laboratory test results in specific constellations.
Objectives: To construct a library that provides interoperable building blocks for the analysis of microbiological laboratory test results.
Methods: We used Java for preprocessing raw microbiological laboratory test results and Arden Syntax for knowledge-based querying of data based on microbiology information elements used in European surveillance criteria for HAIs.
Artif Intell Med
May 2016
University Clinic for Hospital Hygiene and Infection Control, Medical University of Vienna and Vienna General Hospital, Waehringer Guertel 18-20, A-1090 Vienna, Austria.
Background: Many electronic infection detection systems employ dichotomous classification methods, classifying patient data as pathological or normal with respect to one or several types of infection. An electronic monitoring and surveillance system for healthcare-associated infections (HAIs) known as Moni-ICU is being operated at the intensive care units (ICUs) of the Vienna General Hospital (VGH) in Austria. Instead of classifying patient data as pathological or normal, Moni-ICU introduces a third borderline class.
View Article and Find Full Text PDFPLoS One
November 2015
Department of Medicine I, Division of Infectious Diseases and Tropical Medicine, Medical University Vienna, Vienna, Austria; Institut für Tropenmedizin, Universität Tübingen, Tübingen, Germany.
Background: Bacteraemia is a frequent and severe condition with a high mortality rate. Despite profound knowledge about the pre-test probability of bacteraemia, blood culture analysis often results in low rates of pathogen detection and therefore increasing diagnostic costs. To improve the cost-effectiveness of blood culture sampling, we computed a risk prediction model based on highly standardizable variables, with the ultimate goal to identify via an automated decision support tool patients with very low risk for bacteraemia.
View Article and Find Full Text PDFGMS Krankenhhyg Interdiszip
August 2012
Clinical Institute for Hospital Hygiene, Medical University of Vienna, Vienna, Austria.
Background: Bacterial contamination of anesthesia breathing machines and their potential hazard for pulmonary infection and cross-infection among anesthetized patients has been an infection control issue since the 1950s. Disposable equipment and bacterial filters have been introduced to minimize this risk. However, the machines' internal breathing-circuit-system has been considered to be free of micro-organisms without providing adequate data supporting this view.
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