Mining patient flow patterns in a surgical ward
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Surgery is a highly critical and costly procedure, and there is an imperative need to improve the efficiency in surgical wards. Analyzing surgical patient flow and predicting cycle times of different peri-operative phases can help improve the scheduling and management of surgeries. In this paper, we propose a novel approach to mining temporal patterns of surgical patient flow with the use of Bayesian belief networks. We present and compare three classes of probabilistic models and we evaluate them with respect to predicting cycle times of individual phases of patient flow. The results of this study support previous work that surgical times are log-normally distributed. We also show that the inclusion of a clustering pre-processing step improves the performance of our models considerably.
Original language | English |
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Title of host publication | HEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 |
Editors | Federico Cabitza, Ana Fred, Hugo Gamboa |
Publisher | SCITEPRESS (Science and Technology Publications, Lda.) |
Publication date | 2020 |
Pages | 273-283 |
ISBN (Electronic) | 9789897583988 |
Publication status | Published - 2020 |
Event | 13th International Conference on Health Informatics, HEALTHINF 2020 - Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 - Valletta, Malta Duration: 24 Feb 2020 → 26 Feb 2020 |
Conference
Conference | 13th International Conference on Health Informatics, HEALTHINF 2020 - Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 |
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Land | Malta |
By | Valletta |
Periode | 24/02/2020 → 26/02/2020 |
Sponsor | Institute for Systems and Technologies of Information, Control and Communication (INSTICC) |
- Bayesian network, Data mining, Patient flows, Process mining, Surgery, Surgical workflow
Research areas
ID: 250487998