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


Autoři: Daniel A. da Silva aff001;  Carla S. ten Caten aff001;  Rodrigo P. dos Santos aff002;  Flavio S. Fogliatto aff001;  Juliana Hsuan aff003
Působiště autorů: Industrial Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil aff001;  Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil aff002;  Copenhagen Business School, Copenhagen, Denmark aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226272

Souhrn

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).

Klíčová slova:

Adverse events – Algorithms – Data mining – Machine learning – Machine learning algorithms – Preprocessing – Surgical and invasive medical procedures – Text mining


Zdroje

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Článek vyšel v časopise

PLOS One


2019 Číslo 12