Literature DB >> 17911780

Automatic infection detection system.

Ove Granberg1, Johan Gustav Bellika, Eirik Arsand, Gunnar Hartvigsen.   

Abstract

An infected person may be contagious already before the first symptoms appear. This person can, in the period of disease evolution, infect several associated citizens before consulting a general practitioner (GP). Early detection of contagion is therefore important to prevent spreading of diseases. The Automatic Infection Detection (AID) System faces this problem through investigating the hypothesis that the blood glucose (BG) level increases when a person is infected. The first objective of the prototyped version of the AID system was to identify possible BG elevations in the incubation time that could be related to the spread of infectious diseases. To do this, we monitored two groups of people, with and without diabetes mellitus. The AID system analyzed the results and we were able to detect two cases of infection during the study period. The time of detection occurred simultaneous or near the time of onset of symptoms. The detection did not occur earlier for a number of reasons. The most likely one is that the evolution process of an infectious disease is both complicated and involves the immune system and several organs in the body. The investigation with regard to isolating the key relations is therefore considered as a very complex study. Nevertheless, the AID system managed to detect the infection much earlier than what is possible with today's early warning systems for infectious diseases.

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Year:  2007        PMID: 17911780

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


  1 in total

1.  Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System.

Authors:  Ashenafi Zebene Woldaregay; Ilkka Kalervo Launonen; Eirik Årsand; David Albers; Anna Holubová; Gunnar Hartvigsen
Journal:  J Med Internet Res       Date:  2020-08-12       Impact factor: 5.428

  1 in total

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