Skip to main content
Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2020 Oct 2;176:1703–1712. doi: 10.1016/j.procs.2020.09.195

Technology of Supporting Medical Decision-Making Using Evidence-Based Medicine and Artificial Intelligence

Georgy Lebedev a,b,c, Eduard Fartushnyi a, Igor Fartushnyi a, Igor Shaderkin a,b, Herman Klimenko a, Pavel Kozhin a, Konstantin Koshechkin a, Ilya Ryabkov a,b, Vadim Tarasov a, Evgeniy Morozov a, Irina Fomina b
PMCID: PMC7531984  PMID: 33042303

Abstract

Currently, Medical errors are a serious problem when examining patients. Creating information systems that use the capabilities of evidence-based medicine and artificial intelligence methods will allow the doctor to make an informed and proven decision. In this article, the authors offer a description of an information system that solves the problem of supporting medical decision making based on evidence-based medicine. This is achieved by using artificial intelligence methods. This work was supported by a grant from the Ministry of Education and Science of the Russian Federation, a unique project identifier RFMEFI60819X0278.

Keywords: medical decision support systems, evidence-based medicine, intellectual systems, text mining

References

  • 1.Kohn L.T., Corrigan J.M., Donaldson M.S., editors. To Err is Human: Building a Safer Health System. Institute of Medicine (US) Committee on Quality of Health Care in America. National Academies Press (US); Washington (DC): 2000. DOI: 10.17226/9728. [PubMed] [Google Scholar]
  • 2.Blei D.M., Ng A.Y., Jordan M.I. Latent Dirichlet allocation // Journal of Machine Learning Research. - 2003;3:993–1022. [Google Scholar]
  • 3.Distributed Representations of Words and Phrases and their Compositionality. / Tomas Mikolov, Ilya Sutskever, Kai Chen et al. // NIPS / Ed. by Christopher J. C. Burges, L´eon Bottou, Zoubin Ghahramani, Kilian Q. Weinberger. - 2013. - Pp. 3111-3119.
  • 4.Knowledge discovery through directed probabilistic topic models: a survey / Ali Daud, Juanzi Li, Lizhu Zhou, Faqir Muhammad // Frontiers of Computer Science in China. 2010;4(2):280–301. [Google Scholar]
  • 5.Vorontsov K.V., Potapenko A.A. Additive Regularization of Topic Models. Machine Learning Journal. 2015;101:303–323. [Google Scholar]
  • 6.Statistical topic models for multi-label document classification / Timothy N. Rubin, America Chambers. Padhraic Smyth, Mark Steyvers // Machine Learning. 2012;88(1-2):157–208. [Google Scholar]
  • 7.Ivan Vuli´c, Wim Smet, Marie-Francine Moens. Cross-language information retrieval models based on latent topic models trained with document-aligned 140 comparable corpora // Information Retrieval. 2012:1–38. [Google Scholar]
  • 8.Allen I.E., Olkin I. Estimating time to conduct a meta-analysis from number of citations retrieved. JAMA. 1999;282:634–635. doi: 10.1001/jama.282.7.634. [DOI] [PubMed] [Google Scholar]
  • 9.Borah R., Brown A.W., Capers P.L., Kaiser K.A. Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry. BMJ Open. 2017;7:e012545. doi: 10.1136/bmjopen-2016-012545. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Tsafnat G., Dunn A., Glasziou P., Coiera E. The automation of systematic reviews. BMJ. 2013:139–346. doi: 10.1136/bmj.f139. [DOI] [PubMed] [Google Scholar]
  • 11.O’Connor A.M., Tsafnat G., Gilbert S.B., Thayer K.A., Wolfe M.S. Moving toward the automation of the systematic review process: a summary of discussions at the second meeting of International Collaboration for the Automation of Systematic Reviews (ICASR) Syst Rev. 2018;7:3. doi: 10.1186/s13643-017-0667-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Thomas J., Noel-Storr A., Marshall I., Wallace B., McDonald S., Mavergames C. Living systematic reviews: 2. Combining human and machine effort. J Clin Epidemiol. 2017;91:31–37. doi: 10.1016/j.jclinepi.2017.08.011. [DOI] [PubMed] [Google Scholar]

Articles from Procedia Computer Science are provided here courtesy of Elsevier

RESOURCES