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Predicting the occurrence of surgical site infections using text mining and machine learning

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  • Daniel A da Silva
  • Carla S ten Caten
  • Rodrigo P dos Santos
  • Flavio S Fogliatto
  • Juliana Hsuan

Abstract

In this study we propose the use of text mining and machine learning methods to predict and detect Surgical Site Infections (SSIs) using textual descriptions of surgeries and post-operative patients’ records, mined from the database of a high complexity University hospital. SSIs are among the most common adverse events experienced by hospitalized patients; preventing such events is fundamental to ensure patients’ safety. Knowledge on SSI occurrence rates may also be useful in preventing future episodes. We analyzed 15,479 surgery descriptions and post-operative records testing different preprocessing strategies and the following machine learning algorithms: Linear SVC, Logistic Regression, Multinomial Naive Bayes, Nearest Centroid, Random Forest, Stochastic Gradient Descent, and Support Vector Classification (SVC). For prediction purposes, the best result was obtained using the Stochastic Gradient Descent method (79.7% ROC-AUC); for detection, Logistic Regression yielded the best performance (80.6% ROC-AUC).

Suggested Citation

  • Daniel A da Silva & Carla S ten Caten & Rodrigo P dos Santos & Flavio S Fogliatto & Juliana Hsuan, 2019. "Predicting the occurrence of surgical site infections using text mining and machine learning," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-17, December.
    • Handle: RePEc:plo:pone00:0226272
    DOI: 10.1371/journal.pone.0226272
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    References listed on IDEAS

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    1. R Andrew Taylor & Christopher L Moore & Kei-Hoi Cheung & Cynthia Brandt, 2018. "Predicting urinary tract infections in the emergency department with machine learning," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-15, March.
    2. Zhuoran Wang & Anoop D Shah & A Rosemary Tate & Spiros Denaxas & John Shawe-Taylor & Harry Hemingway, 2012. "Extracting Diagnoses and Investigation Results from Unstructured Text in Electronic Health Records by Semi-Supervised Machine Learning," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-9, January.
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