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Supporting the early detection of disease onset and change using document vector analysis of nursing observation records
http://hdl.handle.net/10232/00031779
http://hdl.handle.net/10232/000317796b665c9a-5e73-44b0-a18e-1997adcb2862
名前 / ファイル | ライセンス | アクション |
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Item type | 学位論文 / Thesis or Dissertation(1) | |||||
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公開日 | 2021-08-19 | |||||
タイトル | ||||||
タイトル | Supporting the early detection of disease onset and change using document vector analysis of nursing observation records | |||||
別言語のタイトル | ||||||
その他のタイトル | 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 | |||||
著者 |
小牧, 祥太郎
× 小牧, 祥太郎 |
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著者よみ | ||||||
姓名 | コマキ, ショウタロウ | |||||
別言語の著者 | ||||||
姓名 | KOMAKI, Syuotarou | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題言語 | en | |||||
主題Scheme | Other | |||||
主題 | Narrative Medicine | |||||
キーワード | ||||||
主題言語 | en | |||||
主題Scheme | Other | |||||
主題 | Patient Safety | |||||
キーワード | ||||||
主題言語 | en | |||||
主題Scheme | Other | |||||
主題 | Aspiration Pneumonia | |||||
キーワード | ||||||
主題言語 | en | |||||
主題Scheme | Other | |||||
主題 | Machine Learning | |||||
キーワード | ||||||
主題言語 | en | |||||
主題Scheme | Other | |||||
主題 | Natural Language Processing | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||
資源タイプ | doctoral thesis | |||||
アクセス権 | ||||||
アクセス権 | open access | |||||
アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
要約(Abstract) | ||||||
内容記述タイプ | Other | |||||
内容記述 | 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. | |||||
要約(Abstract) | ||||||
内容記述タイプ | Other | |||||
内容記述 | 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 |
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作成日 | ||||||
日付 | 2021-07-09 | |||||
出版タイプ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
NDC | ||||||
主題Scheme | NDC | |||||
主題 | 490 | |||||
ファイル(説明) | ||||||
内容記述タイプ | Other | |||||
内容記述 | 博士論文全文 | |||||
ファイル(説明) | ||||||
内容記述タイプ | Other | |||||
内容記述 | 博士論文要旨 | |||||
ファイル(説明) | ||||||
内容記述タイプ | Other | |||||
内容記述 | 最終試験結果の要旨 | |||||
ファイル(説明) | ||||||
内容記述タイプ | Other | |||||
内容記述 | 論文審査の要旨 | |||||
公開者・出版者 | ||||||
出版者 | SAGE Journals | |||||
公開者・出版者 | ||||||
出版者 | 鹿児島大学 | |||||
公開者よみ | ||||||
公開者よみ | カゴシマ ダイガク | |||||
公開者別名 | ||||||
公開者別名 | Kagoshima University | |||||
備考 | ||||||
備考 | 【指導教員:橋口照人 , 宇都由美子】 | |||||
date.appl | ||||||
【学位申請日】2021-05-06 | ||||||
学位名 | ||||||
学位名 | 博士(医学)Doctor of Philosophy in Medical Science | |||||
学位授与機関 | ||||||
学位授与機関識別子Scheme | kakenhi | |||||
学位授与機関識別子 | 17701 | |||||
学位授与機関名 | 鹿児島大学 | |||||
学位授与年月日 | ||||||
学位授与年月日 | 2021-07-15 | |||||
学位授与番号 | ||||||
学位授与番号 | 甲総研第614号 |