Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 28 October 2018 / Approved: 29 October 2018 / Online: 29 October 2018 (11:46:18 CET)

A peer-reviewed article of this Preprint also exists.

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

Journal reference: Informatics 2019, 6, 4
DOI: 10.3390/informatics6010004

Abstract

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 6,497 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 the text-based risk score algorithm as predictive of death within 30 days of surgery. We studied the additional performance obtained by including text-based risk score as a predictor of death along with other structured data based clinical risk factors. The C-statistic of a logistic regression model with 5-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.

Subject Areas

post-operative death; unstructured data; logistic regression; text mining; surgery outcome

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