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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationUbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PublisherAssociation for Computing Machinery, Inc
Pages89-92
Number of pages4
ISBN (Electronic)9781450344623
DOIs
Publication statusPublished - Sep 12 2016
Event2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany
Duration: Sep 12 2016Sep 16 2016

Other

Other2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
CountryGermany
CityHeidelberg
Period9/12/169/16/16

Fingerprint

Nursing
Orthopedics
Surgery

All Science Journal Classification (ASJC) codes

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

Cite this

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. In 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.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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. in 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, Germany, 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. In 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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