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

孫 光鎬, 箱崎 幸也, 阿部 重人, 松井 岳巳

doi:10.11239/jsmbe.53.s174_01

書誌事項

タイトル別名

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

説明

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.

収録刊行物

生体医工学

生体医工学 53 (Supplement), S174_01-S174_01, 2015

公益社団法人日本生体医工学会

詳細情報詳細情報について

CRID: 1390282680243093632

NII論文ID: 130005163668

DOI: 10.11239/jsmbe.53.s174_01

ISSN: 18814379; 1347443X

本文言語コード: ja

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