PERTANIKA PROCEEDINGS

Home / Pertanika Proceedings / / J

 

J

J

Pertanika Proceedings, Volume J, Issue J, J

Keywords: J

Published on: J

J

  • AHRQ. (2013). National healthcare disparities report. Agency for Healthcare Research and Quality. http://www.ahrq.gov/research/findings/nhqrdr/nhdr13/chap4.html

  • Alam, A. Y. (2016). Steps in the process of risk management in healthcare. Journal of Epidemiology and Preventive Medicine, 2(2), 1-5.

  • Aven, T., & Eidesen, K. (2007). A predictive Bayesian approach to risk analysis in health care. BMC Medical Research Methodology, 7(1), 1-8.

  • Cho, J. S., Hahn, K. Y., Kwak, J. M., Kim, J., Baek, S. J., Shin, J. W., & Kim, S. H. (2013). Virtual reality training improves da Vinci performance: A prospective trial. Journal of Laparoendoscopic and Advanced Surgical Techniques, 23(12), 992-998. https://doi.org/10.1089/lap.2012.0396

  • DeSouza, A. L., Prasad, L. M., Park, J. J., Marecik, S., Blumetti, J., & Abcarian, H. (2010). Robotic assistance in right hemicolectomy: Is there a role? Journal of Disease of the Colon & Rectum, 53(7), 1000-1006. https://doi.org/10.1007/DCR.0b013e3181d32096

  • Ferson, S., Hajagos, J., Berleant, D., Zhang, J., Tucker, W. T., Ginzburg, L., Nelsen, R., Oberkampf, W. L., & Hall, C. (2004). Dependence in Dempster-Shafer theory and probability bounds analysis. Sandia National Laboratories.

  • Freeman, J. (2022). Event tree analysis - The risk assessment application tool. Edraw. https://www.edrawsoft.com/event-tree-introduction.html

  • Gleason, P. M., & Harris, J. E. (2019). The Bayesian approach to decision making and analysis in nutrition research and practice. Journal of the Academy of Nutrition and Dietetics, 119(12), 1993-2003. https://doi.org/10.1016/j.jand.2019.07.009

  • Lee, J., Nah, K. Y., Kim, R. M., Ahn, Y. H., Soh, E. Y., & Chung, W. Y. (2010). Differences in postoperative outcomes, function, and cosmesis: Open versus robotic thyroidectomy. Surgical Endoscopy, 24, 3186-3194. https://doi.org/10.1007/s00464-010-1113-z

  • Mattos, L. S., Caldwell, D. G., Peretti, G., Mora, F., Guastini, L., & Cingolani, R. (2016). Microsurgery robots: Addressing the needs of high-precision surgical interventions. Swiss Medical Weekly, 146, 1-14. https://doi.org/10.4414/smw.2016.14375

  • Ng, A. T. L., & Tam, P. C. (2014). Current status of robot-assisted surgery. Hong Kong Medical Journal, 20(3), 241-250. https://doi.org/10.12809/hkmj134167

  • Nivolianitou, Z. S., Leopoulos, V. N., & Konstantinidou, M. (2004). Comparison of techniques for accident scenario analysis in hazardous systems. Journal of Loss Prevention in the Process Industries, 17(6), 467-475. https://doi.org/10.1016/j.jlp.2004.08.001

  • Olanrewaju, O. A., Faieza, A. A., & Syakirah, K. (2013). Current trend of robotics application in medical. IOP Conference Series: Materials Science and Engineering, 46, Article 012041. https://doi.org/10.1088/1757-899X/46/1/012041

  • Oppermann, A. (2018). Bayes’ theorem: The holy grail of data science. Towards Data Science. https://towardsdatascience.com/bayes-theorem-the-holy-grail-of-data-science-55d93315defb

  • Park, J. H., Lee, J., Hakim, N. A., Kim, H. Y., Kang, S. W., Jeong, J. J., & Chung, W. Y. (2015). Robotic thyroidectomy learning curve for beginning surgeons with little or no experience of endoscopic surgery. Head and Neck, 37(12), 1705-1711. https://doi.org/10.1002/hed.23824

  • Perez, R. E., & Schwaitzberg, S. D. (2019). Robotic surgery: Finding value in 2019 and beyond. Annals of Laparoscopic and Endoscopic Surgery, 4(3), 1-7. http://dx.doi.org/10.21037/ales.2019.05.02

  • Sadiq, R., Saint-Martin, E., & Kleiner, Y. (2008). Predicting risk of water quality failures in distribution networks under uncertainties using fault-tree analysis. Urban Water Journal, 5(4), 287-304. https://doi.org/10.1080/15730620802213504https://doi.org/10.12809/hkmj134167

  • Sahabudin, R. M., Arni, T., Ashani, N., Arumuga, K., Rajenthran, S., Murali, S., & Menon, M. (2006). Development of robotic program: An Asian experience. World Journal of Urology, 24, 161-164. https://doi.org/10.1007/s00345-006-0069-z

  • Simon, C., Weber, P., & Levrat, E. (2007). Bayesian networks and evidence theory to model complex systems reliability. Journal of Computers, 2(1), 33-43.

  • Sklet, S. (2004). Comparison of some selected methods for accident investigation. Journal of Hazardous Materials, 111(1-3), 29-37. https://doi.org/10.1016/j.jhazmat.2004.02.005

  • Spouge, J. (1999). A guide to quantitative risk assessment for offshore installations. CMPT Publication.

  • Talib, Y. Y. A. (2017, August 28). Pembedahan robotic[Robotic surgery]. Harian Metro. https://www.hmetro.com.my/hati/2017/08/258470/pembedahan-robotik

  • Weaver, A., & Steele, S. (2016) Robotics in colorectal surgery. F1000 Research, 5, Article 2373. https://doi.org/10.12688/f1000research.9389.1

  • Zakaria, A. D., Toh, J. W. T., & Kim, S. H. (2018). Future perspectives in robotic colorectal surgery. In N. Kim, K. Sugihara & J. T. Liang (Eds.), Surgical treatment of colorectal cancer (pp. 315-325). Springer. https://doi.org/10.1007/978-981-10-5143-2_29

  • Zheng, X., & Liu, M. (2009). An overview of accident forecasting methodologies. Journal of Loss Prevention in the Process Industries, 22(4), 484-491. https://doi.org/10.1016/j.jlp.2009.03.005

  • Zoullouti, B., Amghar, M., & Nawal, S. (2019). Using Bayesian networks for risk assessment in healthcare system. In D. McNair (Ed.), Bayesian networks: advances and novel applications (pp. 39-53). IntechOpen. https://doi.org/10.5772/intechopen.80464

ISSN

e-ISSN 3083-9475

Article ID

J

Download Full Article PDF

Share this article

Recent Articles