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Description

Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underreporting as current methodology relies heavily on manual chart review. The rapid adoption of electronic health records (EHRs) has the potential to allow the secondary use of EHR data for quality surveillance programs. The aim of this study is to investigate the effectiveness of integrating natural language processing (NLP) outputs with structured EHR data to build machine learning models for SSI identification using real-world clinical data. We examined a set of models using structured data with and without NLP document-level, mention-level, and keyword features. The top-performing model was based on a Random Forest classifier enhanced with NLP document-level features achieving a 0.58 sensitivity, 0.97 specificity, 0.54 PPV, 0.98 NPV, and 0.52 F0.5 score. We further interrogated the feature contributions, analyzed the errors and discussed future directions.

Learning Objective: The limitations of current Surgical Site Infection (SSI) surveillance methods.
How NLP derived features improve machine learning models' performance
Future directions of automatic SSI surveillance.

Authors:

Jianlin Shi (Presenter)
University of Utah

Siru Liu, University of Utah
Liese Pruitt, University of Utah
Carolyn Luppens, University of Utah
Jeffrey Ferraro, Intermountain Healthcare
Adi Gundlapalli, University of Utah
Wendy Chapman, University of Utah
Brian Bucher, University of Utah

Presentation Materials:

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