Next Article in Journal
Statistical Deadband: A Novel Approach for Event-Based Data Reporting
Next Article in Special Issue
Understanding the EMR-Related Experiences of Pregnant Japanese Women to Redesign Antenatal Care EMR Systems
Previous Article in Journal
Acknowledgement to Reviewers of Informatics in 2018
Previous Article in Special Issue
Large Scale Advanced Data Analytics on Skin Conditions from Genotype to Phenotype
Open AccessArticle

Unstructured Text in EMR Improves Prediction of Death after Surgery in Children

1
UTSHC-ORNL Center for Biomedical Informatics, Department of Pediatrics, Memphis, TN 38103, USA
2
Department of Foundational Medical Studies, Oakland University William Beaumont School of Medicine, Rochester, MI 48309, USA
3
Quire Inc., Memphis, TN 38103, USA
4
Department of Surgery, University of Tennessee Health Science Center, Memphis, TN 38103, USA
*
Author to whom correspondence should be addressed.
Informatics 2019, 6(1), 4; https://doi.org/10.3390/informatics6010004
Received: 28 October 2018 / Revised: 3 January 2019 / Accepted: 5 January 2019 / Published: 10 January 2019
(This article belongs to the Special Issue Data-Driven Healthcare Research)
Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children’s Hospital EMR. We used a text mining approach on preoperative notes to obtain a text-based risk score to predict death within 30 days of surgery. In addition, we evaluated the performance of a hybrid model that included the text-based risk score along with structured data pertaining to clinical risk factors. The C-statistic of a logistic regression model with five-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes. View Full-Text
Keywords: post-operative death; unstructured data; logistic regression; text mining; surgery outcome post-operative death; unstructured data; logistic regression; text mining; surgery outcome
Show Figures

Figure 1

MDPI and ACS Style

Akbilgic, O.; Homayouni, R.; Heinrich, K.; Langham, M.R.; Davis, R.L. Unstructured Text in EMR Improves Prediction of Death after Surgery in Children. Informatics 2019, 6, 4.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop