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Prevalence and predictors of C. difficile infections in hospitalized patients with major surgical procedures in the USA: Analysis using traditional and machine learning methods.

Publication ,  Journal Article
Allareddy, V; Wang, T; Rampa, S; Caplin, J; Nalliah, R; Badheka, A; Allareddy, V
Published in: Am J Surg
September 2019

Duke Scholars

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Published In

Am J Surg

DOI

EISSN

1879-1883

Publication Date

September 2019

Volume

218

Issue

3

Start / End Page

661 / 662

Location

United States

Related Subject Headings

  • United States
  • Surgery
  • Retrospective Studies
  • Prevalence
  • Middle Aged
  • Male
  • Machine Learning
  • Humans
  • Female
  • Cross Infection
 

Citation

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Allareddy, V., Wang, T., Rampa, S., Caplin, J., Nalliah, R., & Badheka, A. (2019). Prevalence and predictors of C. difficile infections in hospitalized patients with major surgical procedures in the USA: Analysis using traditional and machine learning methods. Am J Surg, 218(3), 661–662. https://doi.org/10.1016/j.amjsurg.2018.11.014
Allareddy, Veerajalandhar, Tong Wang, Sankeerth Rampa, Jennifer Caplin, Romesh Nalliah, Aditya Badheka, and Veerasathpurush Allareddy. “Prevalence and predictors of C. difficile infections in hospitalized patients with major surgical procedures in the USA: Analysis using traditional and machine learning methods.Am J Surg 218, no. 3 (September 2019): 661–62. https://doi.org/10.1016/j.amjsurg.2018.11.014.
Allareddy, Veerajalandhar, et al. “Prevalence and predictors of C. difficile infections in hospitalized patients with major surgical procedures in the USA: Analysis using traditional and machine learning methods.Am J Surg, vol. 218, no. 3, Sept. 2019, pp. 661–62. Pubmed, doi:10.1016/j.amjsurg.2018.11.014.
Journal cover image

Published In

Am J Surg

DOI

EISSN

1879-1883

Publication Date

September 2019

Volume

218

Issue

3

Start / End Page

661 / 662

Location

United States

Related Subject Headings

  • United States
  • Surgery
  • Retrospective Studies
  • Prevalence
  • Middle Aged
  • Male
  • Machine Learning
  • Humans
  • Female
  • Cross Infection