Mining patient flow patterns in a surgical ward

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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.

OriginalsprogEngelsk
TitelHEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020
RedaktørerFederico Cabitza, Ana Fred, Hugo Gamboa
ForlagSCITEPRESS (Science and Technology Publications, Lda.)
Publikationsdato2020
Sider273-283
ISBN (Elektronisk)9789897583988
StatusUdgivet - 2020
Begivenhed13th International Conference on Health Informatics, HEALTHINF 2020 - Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 - Valletta, Malta
Varighed: 24 feb. 202026 feb. 2020

Konference

Konference13th International Conference on Health Informatics, HEALTHINF 2020 - Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020
LandMalta
ByValletta
Periode24/02/202026/02/2020
SponsorInstitute for Systems and Technologies of Information, Control and Communication (INSTICC)

ID: 250487998