Automatic Detection of Skin and Subcutaneous Tissue Infections from Primary Care Electronic Medical Records.

Journal: Studies in health technology and informatics

Published Date: Jan 1, 2015

Abstract

INTRODUCTION: Skin and subcutaneous tissue infections (SSTI) are common conditions that cause avoidable hospitalisation in New Zealand. As part of a program to improve the management of SSTI in primary care, electronic medical records (EMR) of four Auckland general practices were analysed to identify SSTI occurrences in the last three years.

Authors

Yulong Gu

University of Auckland, New Zealand.
John Kennelly

University of Auckland, New Zealand.
Jim Warren

University of Auckland, New Zealand.
Pritesh Nathani

University of Auckland, New Zealand.
Tai Boyce

The Fono Health & Social Services, Auckland, New Zealand.

Keywords

Data Mining Diagnosis, Computer-Assisted Electronic Health Records Humans Machine Learning Natural Language Processing New Zealand Primary Health Care Reproducibility of Results Sensitivity and Specificity Skin Diseases, Bacterial Subcutaneous Tissue Vocabulary, Controlled

External Resources

View on PubMed PubMed (26210421)

Automatic Detection of Skin and Subcutaneous Tissue Infections from Primary Care Electronic Medical Records.

Abstract

Authors

Keywords

External Resources

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