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Detection of Surgical Site Infection Utilizing Automated Feature Generation in Clinical Notes

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Abstract

Postsurgical complications (PSCs) are known as a deviation from the normal postsurgical course and categorized by severity and treatment requirements. Surgical site infection (SSI) is one of major PSCs and the most common healthcare-associated infection, resulting in increased length of hospital stay and cost. In this work, we proposed an automated way to generate keyword features using sublanguage analysis with heuristics to detect SSI from cohort in clinical notes and evaluated these keywords with medical experts. To further validate our approach, we also applied different machine learning algorithms on cohort using automatically generated keywords. The results showed that our approach was able to identify SSI keywords from clinical narratives and can be used as a foundation to develop an information extraction system or support search-based natural language processing (NLP) approaches by augmenting search queries.

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Funding

This work was made possible by joint funding from National Institute of Health grants R01GM102282A1 and NIH R01 EB19403-1.

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Authors and Affiliations

  1. Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA

    Feichen Shen, James M. Naessens, Elizabeth B. Habermann, Hongfang Liu & Sunghwan Sohn

  2. Department of Surgery, Mayo Clinic College of Medicine, Rochester, MN, USA

    David W. Larson

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  1. Feichen Shen
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Correspondence to Feichen Shen.

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Shen, F., Larson, D.W., Naessens, J.M. et al. Detection of Surgical Site Infection Utilizing Automated Feature Generation in Clinical Notes. J Healthc Inform Res 3, 267–282 (2019). https://doi.org/10.1007/s41666-018-0042-9

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