Predicting daily nursing load from nurses' activity logs and patients' medical records

Sozo Inoue, Yasuhiko Sugiyama, Tatsuya Isoda, Yasunobu Nohara, Mako Shirouzu, Naoki Nakashima

研究成果: 著書/レポートタイプへの貢献会議での発言

1 引用 (Scopus)

抄録

In this paper, we integrate nurse activity data, location data, and medical records to predict the nursing load of every day, assuming the application for task allocation for nurses. We collected nurse activity data, location data, medical payment data, and nursing needs data in cooperation with one floor of a hospital, which constitutes the orthopedic surgery department, for 40 days, 24 hours per day. With the collected data, we predicted the next day's nursing time for a patient from the previous day's patient status using RandomForest algorithm, and achieved 73.7% of accuracy.

元の言語英語
ホスト出版物のタイトルUbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
出版者Association for Computing Machinery, Inc
ページ89-92
ページ数4
ISBN(電子版)9781450344623
DOI
出版物ステータス出版済み - 9 12 2016
イベント2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, ドイツ
継続期間: 9 12 20169 16 2016

その他

その他2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
ドイツ
Heidelberg
期間9/12/169/16/16

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Nursing
Orthopedics
Surgery

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Software
  • Information Systems
  • Computer Networks and Communications
  • Human-Computer Interaction

これを引用

Inoue, S., Sugiyama, Y., Isoda, T., Nohara, Y., Shirouzu, M., & Nakashima, N. (2016). Predicting daily nursing load from nurses' activity logs and patients' medical records. : UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 89-92). Association for Computing Machinery, Inc. https://doi.org/10.1145/2968219.2971454

Predicting daily nursing load from nurses' activity logs and patients' medical records. / Inoue, Sozo; Sugiyama, Yasuhiko; Isoda, Tatsuya; Nohara, Yasunobu; Shirouzu, Mako; Nakashima, Naoki.

UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2016. p. 89-92.

研究成果: 著書/レポートタイプへの貢献会議での発言

Inoue, S, Sugiyama, Y, Isoda, T, Nohara, Y, Shirouzu, M & Nakashima, N 2016, Predicting daily nursing load from nurses' activity logs and patients' medical records. : UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, pp. 89-92, 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, Heidelberg, ドイツ, 9/12/16. https://doi.org/10.1145/2968219.2971454
Inoue S, Sugiyama Y, Isoda T, Nohara Y, Shirouzu M, Nakashima N. Predicting daily nursing load from nurses' activity logs and patients' medical records. : UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc. 2016. p. 89-92 https://doi.org/10.1145/2968219.2971454
Inoue, Sozo ; Sugiyama, Yasuhiko ; Isoda, Tatsuya ; Nohara, Yasunobu ; Shirouzu, Mako ; Nakashima, Naoki. / Predicting daily nursing load from nurses' activity logs and patients' medical records. UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2016. pp. 89-92
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