Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.

Szczegóły
Abstrakt

Tytuł:: Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.
Autorzy:: Shi J; School of Medicine, University of Utah, Salt Lake City, Utah, US.
Liu S; School of Medicine, University of Utah, Salt Lake City, Utah, US.
Pruitt LCC; School of Medicine, University of Utah, Salt Lake City, Utah, US.
Luppens CL; School of Medicine, University of Utah, Salt Lake City, Utah, US.
Ferraro JP; School of Medicine, University of Utah, Salt Lake City, Utah, US.; Intermountain Healthcare, Salt Lake City, Utah, US.
Gundlapalli AV; School of Medicine, University of Utah, Salt Lake City, Utah, US.; VA Salt Lake City Healthcare System, IDEAS Center 2.0, Salt Lake City, Utah, US.
Chapman WW; School of Medicine, University of Utah, Salt Lake City, Utah, US.
Bucher BT; School of Medicine, University of Utah, Salt Lake City, Utah, US.
Źródło:: AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2020 Mar 04; Vol. 2019, pp. 794-803. Date of Electronic Publication: 2020 Mar 04 (Print Publication: 2019).
Typ publikacji:: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.
Język:: English
Imprint Name(s):: Original Publication: Bethesda, MD : American Medical Informatics Association, c2003-
MeSH Terms:: Electronic Health Records*
Machine Learning*
Natural Language Processing*
Information Storage and Retrieval/*methods
Surgical Wound Infection/*diagnosis
Algorithms ; Decision Trees ; Humans ; Logistic Models ; Sensitivity and Specificity ; Support Vector Machine
References:: J Hosp Infect. 2010 Jul;75(3):178-82. (PMID: 20227139)
Med Care. 2014 Feb;52(2 Suppl 1):S91-6. (PMID: 24430273)
AMIA Annu Symp Proc. 2017 Feb 10;2016:1822-1831. (PMID: 28269941)
Med Care. 2013 Jun;51(6):509-16. (PMID: 23673394)
Am J Manag Care. 2014 Sep;20(9):734-40. (PMID: 25365748)
JAMA. 2015 Feb 3;313(5):483-95. (PMID: 25647204)
J Biomed Semantics. 2016 Jun 23;7(1):42. (PMID: 27338146)
JAMA Surg. 2015 Jan;150(1):51-7. (PMID: 25426765)
Ann Surg. 2009 Sep;250(3):363-76. (PMID: 19644350)
J Am Med Inform Assoc. 2018 Sep 1;25(9):1160-1166. (PMID: 29982511)
J Am Coll Surg. 2017 Jan;224(1):59-74. (PMID: 27915053)
Ann Surg. 2011 Oct;254(4):619-24. (PMID: 22039608)
J Hosp Infect. 2009 Jul;72(3):243-50. (PMID: 19446918)
J Biomed Inform. 2018 Sep;85:106-113. (PMID: 30092358)
Grant Information:: K08 HS025776 United States HS AHRQ HHS
Entry Date(s):: Date Created: 20200421 Date Completed: 20200821 Latest Revision: 20210327
Update Code:: 20240104
PubMed Central ID:: PMC7153106
PMID:: 32308875
: Czasopismo naukowe

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. This study aims 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 F 0.5 score. We further interrogated the feature contributions, analyzed the errors, and discussed future directions.
(©2019 AMIA - All rights reserved.)

Informacja