3.8

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2.4

Impact Factor
  • ISSN 1674-8301
  • CN 32-1810/R
Wang Le Yi, McKelvey George M., Wang Hong. Multi-outcome predictive modelling of anesthesia patients[J]. The Journal of Biomedical Research, 2019, 33(6): 430-434. DOI: 10.7555/JBR.33.20180088
Citation: Wang Le Yi, McKelvey George M., Wang Hong. Multi-outcome predictive modelling of anesthesia patients[J]. The Journal of Biomedical Research, 2019, 33(6): 430-434. DOI: 10.7555/JBR.33.20180088

Multi-outcome predictive modelling of anesthesia patients

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  • Corresponding author:

    Hong Wang, Department of Anesthesiology, West Virginia University, Medical Center Drive, Box 8255, Morgantown, WV 26505, USA. Tel/Fax: 3045984122/3045984930, E-mail: hong.wanh1@wvumedicine.org

  • Received Date: September 14, 2018
  • Revised Date: November 30, 2018
  • Accepted Date: December 07, 2018
  • Available Online: February 18, 2019
  • Conjunctive use of anesthetic agents results in drug interactions which can alter or influence multiple patient outcomes such as anesthesia depth, and cardiorespiratory parameters which can also be altered by patient conditions and surgical procedures. Using artificial intelligence technology to continuously gather data of drug infusion and patient outcomes, we can generate reliable computer models individualized for a patient during specific stages of particular surgical procedures. This data can then be used to extend the current anesthesia monitoring functions to include future impact prediction, drug administration planning, and anesthesia decisions.
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    Corresponding author: Wang Hong, hong.wanh1@wvumedicine.org

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