Predicting the Occurrence of Surgical Site Infections Using Text Mining and Machine Learning

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

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

26 Downloads (Pure)


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).
Original languageEnglish
Article numbere0226272
JournalPLoS ONE
Issue number12
Number of pages17
Publication statusPublished - 13 Dec 2019

Cite this