Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011 |
| Pages | 913-916 |
| Number of pages | 4 |
| Volume | 2 |
| DOIs | |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai Duration: 2011 Jul 26 → 2011 Jul 28 |
Other
| Other | 2011 7th International Conference on Natural Computation, ICNC 2011 |
|---|---|
| City | Shanghai |
| Period | 11/7/26 → 11/7/28 |
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ASJC Scopus subject areas
- Computational Theory and Mathematics
- Neuroscience(all)
Cite this
Mental state detection and tagging in nursing records. / Scheidel, Antonia; Zufri, Ahmad; Hashimoto, Kazuo.
Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011. Vol. 2 2011. p. 913-916 6022279.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Mental state detection and tagging in nursing records
AU - Scheidel, Antonia
AU - Zufri, Ahmad
AU - Hashimoto, Kazuo
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80053396836&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053396836&partnerID=8YFLogxK
U2 - 10.1109/ICNC.2011.6022279
DO - 10.1109/ICNC.2011.6022279
M3 - Conference contribution
AN - SCOPUS:80053396836
SN - 9781424499533
VL - 2
SP - 913
EP - 916
BT - Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
ER -