Rapid screening of influenza patients with severe clinical signs using an extensible neural-network-based infection screening system

DOI

Bibliographic Information

Other Title
  • 自己組織化ニューラルネットワークを用いたバイタルサインデータ解析によるインフルエンザの重症度判定

Description

The outbreak of emerging infectious diseases frequently becomes severe are threatening people on global basis. To classify people into severe-influenza patients, mildly-influenza patients and healthy people at places of mass gathering, we developed an influenza screening system. The system conducts screening within 10 s from vital-signs, i.e., respiration rate, heart rate, and facial temperature. A neural network based discriminant function was implemented into the system to predict the infected individuals. We conducted influenza screening for 35 seasonal influenza patients at the Japan Self-defense Central Hospital. To assess the clustering performance of this system, SpO2 was measured as a reference. The system classified 34/35 influenza patients to be mildly or severe influenza. The 10/17 severe-influenza group indicated SpO2 less than 96%, while, only 2/17 mildly-influenza group showed SpO2 less than 96%. This result indicates that the system has the potential to serve as a helpful tool for rapid screening of influenza in clinical settings at places of mass gathering.

Journal

Details 詳細情報について

  • CRID
    1390282680243093632
  • NII Article ID
    130005163668
  • DOI
    10.11239/jsmbe.53.s174_01
  • ISSN
    18814379
    1347443X
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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