| Project/Area Number |
18K10249
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 58050:Fundamental of nursing-related
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| Research Institution | Shonan Kamakura University of Medical Sciences (2021-2022)
Tokyo University of Information Sciences (2018-2020) |
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Principal Investigator |
Oishi Tomoko 湘南鎌倉医療大学, 看護学部, 講師 (40413257)
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| Co-Investigator(Kenkyū-buntansha) |
川口 孝泰 医療創生大学, 国際看護学部, 教授 (40214613)
今井 哲郎 長崎大学, 工学研究科, 助教 (10436173)
伊藤 嘉章 医療創生大学, 国際看護学部, 准教授 (60804870)
豊増 佳子 東京情報大学, 看護学部, 講師 (60276657)
菊谷 武 日本歯科大学, 生命歯学部, 教授 (20214744)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | 摂食嚥下リハビリテーション / 生体情報 / モニタリング / 誤嚥 / 生体情報モニタリング / 測定具の検証 / 摂食嚥下障害 / 食行動 / 誤嚥性肺炎 |
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| Outline of Final Research Achievements |
The present study aimed to identify advanced warning signs of aspiration pneumonia by collecting basic demographic information, biological data, and facial coordinate data of patients with dysphagia and extracting relevant factors and features (e.g., oxygen saturation [SpO2], fingertip plethysmography, lip/jaw coordinates) to develop a monitoring technique that detects signs of aspiration pneumonia via machine learning.
Attempts were made to collect the following information: basic information; FIM; vital signs; biosignal monitoring (SpO2, fingertip plethysmography, coordinates of the lips, corners of the mouth, and jaw via face tracking). However, the coronavirus disease 2019 pandemic resulted in a delay in beginning the examinations. Nevertheless, the authors are currently partnered with a facility where they are conducting the survey and are in the process of data collection and analysis.
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| Academic Significance and Societal Importance of the Research Achievements |
観察によって得られた生体情報を蓄積して、判別式の的中率を向上させ、機械学習によって最適な特徴量選択することができれば、看護実践において有効なモニタリング手法の開発につながる。このモニタリング手法の活用により、摂食嚥下障害患者の安全な経口摂取に向けた、エビデンスに基づく援助計画の立案につながると同時に、安全で、かつ、積極的な摂食嚥下リハビリテーションが可能となる。
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