Assessing surgical site infection risk factors using electronic medical records and text mining

Assessing surgical site infection risk factors using electronic medical records and text mining

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...

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Veröffentlicht in:American journal of infection control 2014-03, Vol.42 (3), p.333-336
Hauptverfasser: Michelson, James D., MD, Pariseau, Jenna S., BA, Paganelli, William C., MD
Format: Artikel
Sprache:eng
Schlagworte:
Adult
Aged
Biological and medical sciences
Cohort Studies
Data Mining
Electronic health records
Electronic Health Records - statistics & numerical data
Epidemiologic Methods
Epidemiology. Vaccinations
Female
General aspects
Human infectious diseases. Experimental studies and models
Humans
Infection Control
Infections
Infectious Disease
Infectious diseases
Male
Medical sciences
Middle Aged
Risk Assessment
Risk factors
Surgery
Surgical Wound Infection - epidemiology
Surveillance
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Beschreibung
Zusammenfassung: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.
ISSN:0196-6553
1527-3296
DOI:10.1016/j.ajic.2013.09.007