Assessing Surgical Site Infection Risk Factors Using Electronic Medical Records and Text Mining

Overview

Journal Am J Infect Control

Specialty Infectious Diseases

Date 2014 Jan 11

PMID 24406258

Citations 8

Authors

James D Michelson

Jenna S Pariseau

William C Paganelli

Affiliations

Soon will be listed here.

Abstract

Text mining techniques to detect surgical site infections (SSI) in unstructured clinical notes were used to improve SSI detection. In conjuction with data from an integrated electronic medical record, all of the 22 SSIs detected by traditional hospital-based surveillance were found using text mining, along with an additional 37 SSIs not detected by traditional surveillance.

Citing Articles

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Irgang L, Barth H, Holmen M J Healthc Inform Res. 2023; 7(1):1-41.

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Performance of machine learning algorithms for surgical site infection case detection and prediction: A systematic review and meta-analysis.

Wu G, Khair S, Yang F, Cheligeer C, Southern D, Zhang Z Ann Med Surg (Lond). 2022; 84:104956.

PMID: 36582918 PMC: 9793260. DOI: 10.1016/j.amsu.2022.104956.

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Streefkerk H, Verkooijen R, Bramer W, Verbrugh H Euro Surveill. 2020; 25(2).

PMID: 31964462 PMC: 6976884. DOI: 10.2807/1560-7917.ES.2020.25.2.1900321.

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

da Silva D, Ten Caten C, Dos Santos R, Fogliatto F, Hsuan J PLoS One. 2019; 14(12):e0226272.

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Identifying the risk factors for catheter-associated urinary tract infections: a large cross-sectional study of six hospitals.

Letica-Kriegel A, Salmasian H, Vawdrey D, Youngerman B, Green R, Furuya E BMJ Open. 2019; 9(2):e022137.

PMID: 30796114 PMC: 6398917. DOI: 10.1136/bmjopen-2018-022137.