看護観察記録の文書ベクトル解析を用いた発症・変化の早期発見支援

• Material Type: 博士論文
• Title: 看護観察記録の文書ベクトル解析を用いた発症・変化の早期発見支援
• Author/Editor: 小牧, 祥太郎
• Author Heading: 小牧, 祥太郎 コマキ, ショウタロウ
• Publication, Distribution, etc.: SAGE Journals
• Alternative Title: Supporting the early detection of disease onset and change using document vector analysis of nursing observation records
• Degree grantor/type: 鹿児島大学
• Date Granted: 2021-07-15
• Dissertation Number: 甲総研第614号
• Degree Type: 博士(医学); Doctor of Philosophy in Medical Science
• Text Language Code: eng
• Subject Heading: Narrative Medicine; Patient Safety; Aspiration Pneumonia; Machine Learning; Natural Language Processing
• Target Audience: 一般
• Note (General): 博士論文全文, 博士論文要旨, 最終試験結果の要旨, 論文審査の要旨; Nursing records are an account of patient condition and treatment during their hospital stay. In this study, we developed a system that can automatically analyze nursing records to predict the occurrence of diseases and incidents (e.g., falls). Text vectorization was performed for nursing records and compared with past case data on aspiration pneumonia, to develop an onset prediction system. Nursing records for a patient group that developed aspiration pneumonia during hospitalization and a non-onset control group were randomly assigned to definitive diagnostic (for learning), preliminary survey, and test datasets. Data from the preliminary survey were used to adjust parameters and influencing factors. The final verification used the test data and revealed the highest compatibility to predict the onset of aspiration pneumonia (sensitivity = 90.9%, specificity = 60.3%) with the parameter values of size = 80 (number of dimensions of the sentence vector), window = 13 (number of words before and after the learned word), and min_count = 2 (threshold of wordcount for word to be included). This method represents the foundation for a discovery/warning system using machine-based automated monitoring to predict the onset of diseases and prevent adverse incidents such as falls. Shotaro Komaki, Fuminori Muranaga, Yumiko Uto, Takashi Iwaanakuchi, Ichiro Kumamoto Supporting the Early Detection of Disease Onset and Change Using Document Vector Analysis of Nursing Observation Records Evaluation & the Health Professions 2021 https://doi.org/10.1177/01632787211014270
• Format (IMT): application/pdf
• Access Restrictions: インターネット公開
• Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• Original Data Provider (Database): 鹿児島大学 : 鹿児島大学リポジトリ

Table of Contents

Result_komaki_syuotarou_isk_614 other

Comments_komaki_syoutarou_isk_614 other

Diss_小牧_祥太郎_ISK614_2021 fulltext

Abstract_小牧_祥太郎_4515800057_2021 abstract