Literature DB >> 19745475

Artificial-intelligence-based hospital-acquired infection control.

Klaus-Peter Adlassnig1, Alexander Blacky, Walter Koller.   

Abstract

Nosocomial or hospital-acquired infections (NIs) are a frequent complication in hospitalized patients. The growing availability of computerized patient records in hospitals permits automated identification and extended monitoring for signs of NIs. A fuzzy- and knowledge-based system to identify and monitor NIs at intensive care units (ICUs) according to the European Surveillance System HELICS (NI definitions derived from the Centers of Disease Control and Prevention (CDC) criteria) was developed and put into operation at the Vienna General Hospital. This system, named Moni, for monitoring of nosocomial infections contains medical knowledge packages (MKPs) to identify and monitor various infections of the bloodstream, pneumonia, urinary tract infections, and central venous catheter-associated infections. The MKPs consist of medical logic modules (MLMs) in Arden syntax, a medical knowledge representation scheme, whose definition is part of the HL7 standards. These MLM packages together with the Arden software are well suited to be incorporated in medical information systems such as hospital information or intensive-care patient data management systems, or in web-based applications. In terms of method, Moni contains an extended data-to-symbol conversion with several layers of abstraction, until the top level defining NIs according to HELICS is reached. All included medical concepts such as "normal", "increased", "decreased", or similar ones are formally modeled by fuzzy sets, and fuzzy logic is used to process the interpretations of the clinically observed and measured patient data through an inference network. The currently implemented cockpit surveillance connects 96 ICU beds with Moni and offers the hospital's infection control department a hitherto unparalleled NI infection survey.

Entities:  

Mesh:

Year:  2009        PMID: 19745475

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  7 in total

Review 1.  Data elements and validation methods used for electronic surveillance of health care-associated infections: a systematic review.

Authors:  Kenrick D Cato; Bevin Cohen; Elaine Larson
Journal:  Am J Infect Control       Date:  2015-06       Impact factor: 2.918

Review 2.  Expert systems in clinical microbiology.

Authors:  Trevor Winstanley; Patrice Courvalin
Journal:  Clin Microbiol Rev       Date:  2011-07       Impact factor: 26.132

3.  Fully Automated Surveillance of Healthcare-Associated Infections with MONI-ICU: A Breakthrough in Clinical Infection Surveillance.

Authors:  A Blacky; H Mandl; K-P Adlassnig; W Koller
Journal:  Appl Clin Inform       Date:  2011-09-14       Impact factor: 2.342

Review 4.  Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis.

Authors:  S Velupillai; D Mowery; B R South; M Kvist; H Dalianis
Journal:  Yearb Med Inform       Date:  2015-08-13

5.  Effectiveness of an automated surveillance system for intensive care unit-acquired infections.

Authors:  Jeroen S de Bruin; Klaus-Peter Adlassnig; Alexander Blacky; Harald Mandl; Karsten Fehre; Walter Koller
Journal:  J Am Med Inform Assoc       Date:  2012-08-07       Impact factor: 4.497

6.  Integrating Arden-Syntax-based clinical decision support with extended presentation formats into a commercial patient data management system.

Authors:  Stefan Kraus; Ixchel Castellanos; Dennis Toddenroth; Hans-Ulrich Prokosch; Thomas Bürkle
Journal:  J Clin Monit Comput       Date:  2013-01-26       Impact factor: 2.502

Review 7.  Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review.

Authors:  Kathrin Seibert; Dominik Domhoff; Dominik Bruch; Matthias Schulte-Althoff; Daniel Fürstenau; Felix Biessmann; Karin Wolf-Ostermann
Journal:  J Med Internet Res       Date:  2021-11-29       Impact factor: 5.428

  7 in total

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