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 | PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 5: HEALTHINF |
Editors | F Cabitza, A Fred, H Gamboa |
Number of pages | 11 |
Publisher | SCITEPRESS Digital Library |
Publication date | 2020 |
Pages | 273-283 |
DOIs | |
Publication status | Published - 2020 |
Event | 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Valletta, Malta Duration: 24 Feb 2020 → 26 Feb 2020 |
Conference
Conference | 13th International Joint Conference on Biomedical Engineering Systems and Technologies |
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Land | Malta |
By | Valletta |
Periode | 24/02/2020 → 26/02/2020 |
- Bayesian Network, Data Mining, Patient Flows, Process Mining, Surgery, Surgical Workflow, CLINICAL PATHWAYS, WORKFLOW, DISCOVERY, CARE
Research areas
ID: 271764759