Mental state detection and tagging in nursing records

Antonia Scheidel*, Ahmad Zufri, Kazuo Hashimoto

*この研究の対応する著者

研究成果

抄録

Staff at geriatric care facilities compile nursing records, containing information from patients' vital signs or treatments suggested by doctors, to comments about patients interactions with the nursing staff, their families and other patients. Especially the latter type of entries often seems to include clues to patients' emotional well-being. Following the assumption that physical and mental health exert a mutual influence on each other, the authors believe that explicitly monitoring patients' emotions and moods can enhance the understanding of changes in physical health. It may also assist nurses in, e.g., preventing negative emotional states like persistent depression affecting patients' overall health for the worse. This paper proposes a strategy to use machine learning techniques to detect and classify emotion in nursing records. Since a first annotation step revealed that entries containing direct speech seem to be especially "emotionally salient", special focus of our future work will be on those entries.

本文言語English
ホスト出版物のタイトルProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
ページ913-916
ページ数4
DOI
出版ステータスPublished - 2011 10 6
外部発表はい
イベント2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, China
継続期間: 2011 7 262011 7 28

出版物シリーズ

名前Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
2

Conference

Conference2011 7th International Conference on Natural Computation, ICNC 2011
国/地域China
CityShanghai
Period11/7/2611/7/28

ASJC Scopus subject areas

  • 計算理論と計算数学
  • 神経科学(全般)

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