An Interaction-Based Bayesian Network Framework for Surgical Workflow Segmentation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experiment Setup and Data Collection
2.2. Interaction Measurement between Surgical Staff
2.3. Bayesian Network-Based Surgical Phase Classification
3. Results
3.1. Spatial and Temporal Patterns between Different Surgical Staff
3.2. Trajectories and Interactions between Different Surgical Staff
3.3. Bayesian Network-Based Surgical Phase Classification
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ramesh, S.; Dall’Alba, D.; Gonzalez, C.; Yu, T.; Mascagni, P.; Mutter, D.; Marescaux, J.; Fiorini, P.; Padoy, N. Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures. ar** and mobile phone call record data for strategic malaria elimination planning. Malar. J. 2014, 13, 52. [Google Scholar] [CrossRef]
- Lee, J.Y.; Kwan, M.P. Visualization of Socio-Spatial Isolation Based on Human Activity Patterns and Social Networks in Space-Time. Tijdschr. Voor Econ. Soc. Geogr. 2011, 102, 468–485. [Google Scholar] [CrossRef]
- Sevtsuk, A.; Ratti, C. Does urban mobility have a daily routine? Learning from the aggregate data of mobile networks. J. Urban Technol. 2010, 17, 41–60. [Google Scholar] [CrossRef]
- Yang, S.; Yang, X.; Zhang, C.; Spyrou, E. Using social network theory for modeling human mobility. IEEE Netw. 2010, 24, 6–13. [Google Scholar] [CrossRef]
- Vlachos, M.; Kollios, G.; Gunopulos, D. Discovering similar multidimensional trajectories. In Proceedings of the 18th Int’l Conf. on Data Engineering, San Jose, CA, USA, 26 February–1 March 2002; pp. 673–684. [Google Scholar]
- Zhang, K.; Taylor, M.A. Effective arterial road incident detection: A Bayesian network based algorithm. Transp. Res. C Emerg. Technol. 2006, 14, 403–417. [Google Scholar] [CrossRef]
- Korb, K.B.; Nicholson, A.E. Bayesian Artificial Intelligence; CRC press: Boca Raton, FL, USA, 2011. [Google Scholar]
- Daly, R.; Shen, Q.; Aitken, S. Review: Learning Bayesian networks: Approaches and issues. Knowl. Eng. Rev. 2011, 26, 99–157. [Google Scholar] [CrossRef] [Green Version]
- Kocabas, V.; Dragicevic, S. Bayesian networks and agent-based modeling approach for urban land-use and population density change: A BNAS model. J. Geogr. Syst. 2008, 15, 403–426. [Google Scholar] [CrossRef]
- Barton, D.N.; Saloranta, T.; Moe, S.J.; Eggestad, H.O.; Kuikka, S. Bayesian belief networks as a meta-modeling tool in integrated river basin management—Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin. Ecol. Econ. 2008, 66, 91–104. [Google Scholar] [CrossRef]
- Larrañaga, P.; Karshenas, H.; Bielza, C.; Santana, R. A review on evolutionary algorithms in Bayesian network learning and inference tasks. Inf. Sci. 2013, 233, 109–125. [Google Scholar] [CrossRef]
- Margaritis, D. Learning Bayesian Network Model Structure from Data; Carnegie Mellon University School of Computer Science: Pittsburgh, PA, USA, 2003. [Google Scholar]
- Gámez, J.A.; Mateo, J.L.; Puerta, J.M. Learning Bayesian networks by hill climbing: Efficient methods based on progressive restriction of the neighborhood. Data Min. Knowl. Disc. 2011, 22, 106–148. [Google Scholar] [CrossRef]
- Downs, J.A.; Horner, M.W. A Characteristic-Hull Based Method for Home Range Estimation. Trans. Gis. 2009, 13, 527–537. [Google Scholar] [CrossRef]
- Heaton, J. Bayesian Networks for Predictive Modeling. Forecast. Futur. 2013, 7, 6–10. [Google Scholar]
- Gonzalez, M.C.; Hidalgo, C.A.; Barabasi, A.L. Understanding individual human mobility patterns. Nature 2008, 453, 779–782. [Google Scholar] [CrossRef]
- Palmer, J.R.; Espenshade, T.J.; Bartumeus, F.; Chung, C.Y.; Ozgencil, N.E.; Li, K. New approaches to human mobility: Using mobile phones for demographic research. Demography 2013, 50, 1105–1128. [Google Scholar] [CrossRef] [Green Version]
- Wong, D.W.; Shaw, S.L. Measuring segregation: An activity space approach. J. Geogr. Syst. 2011, 13, 127–145. [Google Scholar] [CrossRef] [PubMed] [Green Version]
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Luo, N.; Nara, A.; Izumi, K. An Interaction-Based Bayesian Network Framework for Surgical Workflow Segmentation. Int. J. Environ. Res. Public Health 2021, 18, 6401. https://doi.org/10.3390/ijerph18126401
Luo N, Nara A, Izumi K. An Interaction-Based Bayesian Network Framework for Surgical Workflow Segmentation. International Journal of Environmental Research and Public Health. 2021; 18(12):6401. https://doi.org/10.3390/ijerph18126401
Chicago/Turabian StyleLuo, Nana, Atsushi Nara, and Kiyoshi Izumi. 2021. "An Interaction-Based Bayesian Network Framework for Surgical Workflow Segmentation" International Journal of Environmental Research and Public Health 18, no. 12: 6401. https://doi.org/10.3390/ijerph18126401